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@Article{Hadjiprocopis94, author = "A.~Hadjiprocopis and P.~Smith and R.~Comley and S.~Lakkos", title = "A Neural Network Scheme for Earthquake prediction based on the Seismic Electric Signals", journal = "{IEEE} Conference on Neural Networks and Signal Processing", year = "1994" } @Article{CANCELHadjiprocopis95, author = "G.~Alusi and A.~Hadjiprocopis and A.~Linney and A.~Wright", title = "Three Dimensional Tracking with Ultrasound for Virtual Reality applications in Surgery", journal = "Digital Signal Processing", year = "1995", } @InCollection{Hadjiprocopis97a, author = "A.~Hadjiprocopis and P.~Smith", title = "Feed Forward Neural Network Entities", booktitle = "Lecture Notes in Computer Science: Biological and Artificial Computation: From Neuroscience to Technology", editor = "J.~Mira and R.~Moreno-Diaz and J.~Cabestany", publisher = "Springer--Verlag", year = "1997", pages = "349--359", abstract = "IWANN 97, Lanzarote" } @InProceedings{Hadjiprocopis97b, author = "A.~Hadjiprocopis and P.~Smith", title = "Feed Forward Neural Network Entities in the analysis of high-dimensional data", booktitle = "Proceedings of the $3^{rd}$ International Conference on Neural Networks and their Applications", year = "1997", pages = "147--154", abstract = "NEURAP 97, Marseilles" } @InProceedings{Hadjiprocopis98, author = "A.~Hadjiprocopis and P.~Smith", title = "Feed Forward Neural Network Entities in Time Series Prediction and Image Classification", booktitle = "Proceeding of the International ICSC/IFAC Symposium on Neural Computation", year = "1998", address = "Vienna", pages = "1002--1008", abstract = "NC 98, Vienna" } @misc{Hadjiprocopis93, author = "A.~Hadjiprocopis", title = "A Neural Network Implementation on a Transputer System and Applications", school = "Electrical Engineering Department, The City University", year = "1993", address = "Northampton Square, London, EC1V 0HB", month = "Apr", note = "{B.Eng.} thesis" } @misc{Hadjiprocopis2000, author = "A.~Hadjiprocopis", title = "Feed Forward Neural Network Entities", school = "School of Informatics, The City University", year = "2000", address = "Northampton Square, London, EC1V 0HB", month = "Sep", note = "{Ph.D.} thesis" } @manual{npmanual97, title = "The {\em np\hspace{-1.33\fontdimen6\font} np\hspace{0.45\fontdimen6\font}} script language and interpreter", author = "A.~Hadjiprocopis", address = "Room A528, The City University, London EC1V 0HB, livantes@@soi.city.ac.uk, http://www.soi.city.ac.uk\~livantes/home.html", year = "1997", month = "Jun" } @Book{Arbib87, author = "M.~A.~Arbib", title = "Brains, Machines, and Mathematics", edition = "2nd", publisher = "Springer--Verlag", address = "New York, NY", year = "1987", } @Book{BoseLiang96, author = "N.~Bose and P.~Liang", title = "Neural Network Fundamentals: graphs, algorithms and applications", publisher = "McGraw-Hill", year = "1996", abstract = "Good mathematical analysis of the perceptron and FFNN, minsky's order of a predicate can be found here." } @Book{Haykin94, author = "S.~Haykin", title = "Neural Networks", edition = "1st", publisher = "Macmillan", address = "Canada", year = "1994" } @InProceedings{Nielsen89, author = "R.~Hecht-Nielsen", title = "Theory of the Backpropagation Neural Network", booktitle = "Proceedings of the International Joint Conference on Neural Networks", volume = "1", pages = "593--606", year = "1989", } @Book{Nielsen90, author = "R.~Hecht-Nielsen", title = "Neurocomputing", publisher = "Addison--Wesley", address = "Menlo Park, CA", year = "1990", abstract = "Another good book on the theory of neural networks with a deep mathematical approach.", } @Article{Knight90, author = "K.~Knight", title = "Connectionist ideas and algorithms", journal = "Communications of the ACM", volume = "33", number = "11", pages = "59--74", year = "1990", } @InCollection{McClelland:Rumelhart:Hinton86, author = "J.~L.~McClelland D.~E.~Rumelhart and G.~E.~Hinton", editor = "D.E.~Rumelhart and J.L.~McClelland", title = "The Appeal of Parallel Distributed Processing", booktitle = "Parallel Distributed Processing: {E}xplorations in the Microstructure of Cognition, Vol. 1: {F}oundations", publisher = "MIT Press", address = "Cambridge, MA", pages = "3--44", year = "1986", } @Book{Minsky69, author = "M.~Minsky and S.~Papert", title = "Perceptrons", publisher = "MIT Press", address = "Cambridge, MA", year = "1969", abstract = "A neat hatchet job by the leaders of the 'Symbolic Programming' school of Artificial Intelligence, on the 'Network' school; probably responsible for the latter appearing to be brain dead for the next decade. Contains extensive mathematical analysis of 1-layer networks.", } @Book{Minsky:Papert88, author = "M.~Minsky and S.~Papert", title = "Perceptrons: {A}n Introduction to Computational Geometry", edition = "Expanded", publisher = "MIT Press", address = "Cambridge, MA", year = "1988", abstract = "A continuation of their first book but stronger. In a way an answer to McClelland's and Rumelhart's PDP book series. Pay special attention to the last chapter, the Epilog, where they make some suggestions about Connectionism", } @Book{Rumelhart:McClelland86a, author = "D.~E.~Rumelhart and J.~L.~McClelland", title = "Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations", publisher = "MIT Press", address = "Cambridge, MA", volume = "1", year = "1986", abstract = "This is {"}the Bible{"} in the field. Chapters by the authors, F.H.C. Crick, L. Elman, G. Hinton, M. Jordan, A.H. Kawamoto, P.W. Munro, D.A. Norman, D.E. Rabin, T. Sejnowski, P. Smolensky, G.Stone, R.J. Williams, D. Zipser. Partial contents list: Vol. 1 -- Part I: The PDP Perspective (4 chapters), Part II: Basic Mechanisms Part III: Formal Analyses Vol. 2 -- Part IV: Psychological processes, Part V: Biological Mechanisms, Part VI: Conclusion.", } @Book{Rumelhart:McClelland86b, author = "D.~E.~Rumelhart and J.~L.~McClelland", title = "Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Psychological and Biological Models", publisher = "MIT Press", address = "Cambridge, MA", volume = "2", year = "1986", } @InCollection{Rumelhart:Hinton:McClelland86c, author = "D.~E.~Rumelhart and G.~E.~Hinton and J.~L.~McClelland", title = "A general framework for parallel distributed processing", booktitle = "Parallel Distributed Processing: Explorations in the Microstructure of Cognition", editor = "D.E.~Rumelhart and J.L.~McClelland", publisher = "MIT Press", address = "Cambridge, MA", pages = "45--76", year = "1986", } @InCollection{Rumelhart:Hinton:Williams86d, author = "D.~E.~Rumelhart and G.~E.~Hinton and R.~J.~Williams", title = "Learning Internal Representations by Back-Propagating Errors", booktitle = "Parallel Distributed Processing: Explorations in the Microstructure of Cognition", editor = "D.E.~Rumelhart and J.L.~McClelland", publisher = "MIT Press", address = "Cambridge, MA", year = "1986", } @Article{Rumelhart:Hinton:Williams86e, author = "D.~E.~Rumelhart and G.~E.~Hinton and R.~J.~Williams", title = "Learning Representations by Back-Propagating Errors", pages = "533--536", journal = "Nature", volume = "323", year = "1986", } @InProceedings{Sietsma88, author = "J.~Sietsma and R.~J.~F.~Dow", title = "Neural Net Pruning---Why and How", pages = "325--333", booktitle = "IEEE International Conference on Neural Networks", volume = "1", address = "San Diego", publisher = "IEEE, New York", year = "1988", } @InProceedings{Sutton86, author = "R.~S.~Sutton", title = "Two Problems with Back--Propagation and Other Steepest--Descent Learning Procedures for Networks", booktitle = "Proceedings of the Eighth Annual Conference of the Cognitive Science Society", pages = "823--831", year = "1986", } @InProceedings{Weigend:Rumelhart:Huberman90, author = "A.~S.~Weigend and D.~E.~Rumelhart and B.~A.~Huberman", title = "Back--propagation, weight elimination and time series prediction", booktitle = "Proceedings of the 1990 Connectionist Models Summer School", publisher = "Morgan Kaufmann", pages = "65--80", year = "1990", } @InProceedings{Weigend91, author = "A.~S.~Weigend and B.~A.~Huberman and D.~E.~Rumelhart", title = "Predicting sunspots and exchange rates with connectionist networks", booktitle = "Nonlinear Modeling and Forecasting, SFI Studies in the Sciences of Complexity", volume = "12", editor = "M.~Casdagli and S.~Eubank", publisher = "Addison--Wesley", year = "1991", } @Book{Werbos74, author = "P.~J.~Werbos", title = "Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences", publisher = "Doctoral Dissertation, Applied Mathematics, Harvard University", address = "Boston, MA", month = nov, year = "1974", abstract = "This is an attempt to introduce and explore back-propagation. The emergence of this new learning algorithm for multilayer perceptrons initiated the new interest in neural nets.", } @InProceedings{Werbos88, author = "P.~JWerbos", title = "Back-Propacation: Past and Future", booktitle = "Proceedings of IEEE International Conference on Neural Networks", volume = "1", pages = "343--353", publisher = "IEEE Press, New York", year = "1988", } @Book{Anderson88, editor = "J. A.~Anderson and E.~Rosenfeld", title = "Neurocomputing: Foundations of Research", publisher = "MIT Press", address = "Cambridge", year = "1988", abstract = "A collection of fine papers and book-extracts on the field of Connectionism and Cognitive Science. It is in the City University Library.", } @Article{Baldi89, author = "P.~Baldi and K.~Hornik", title = "Neural Networks and Principal Component Analysis: Learning from Examples Without Local Minima", pages = "53--58", journal = "Neural Networks", volume = "2", year = "1989", } @Article{Caianiello61, author = "E.~R.~Caianiello", title = "Outline of a Theory of Thought and Thinking Machines", pages = "204--235", journal = "Journal of Theoretical Biology", volume = "1", year = "1961", } @Article{Feldman82, author = "J.~A.~Feldman and D.~H.~Ballard", title = "Connectionist Models and Their Properties", journal = "Cognitive Science", volume = "6", year = "1982", } @Book{Hebb49, author = "D.~O.~Hebb", title = "The Organization of Behavior", publisher = "Wiley", address = "New York", year = "1949", abstract = "This is the book that started it all: Hebbian learning", } @InCollection{Hornik:Stinchcombe:White92, author = "K.~Hornik and M.~Stinchcombe and H.~White", title = "Multilayer Feedforward Networks Are Universal Approximators", booktitle = "Artificial Neural Networks: Approximation and Learning Theory", editor = "Halber White", publisher = "Blackwell", address = "Oxford, UK", year = "1992", pages = "12--28" } @InCollection{Gallant:White92, author = "A.~R.~Gallant and H.~White", title = "There Exists a Neural Network That Does Not Make Avoidable Mistakes", booktitle = "Artificial Neural Networks: Approximation and Learning Theory", editor = "Halber White", publisher = "Blackwell", address = "Oxford, UK", year = "1992", pages = "5--11" } @Book{Jolliffe86, author = "I.~T.~Jolliffe", title = "Principal Component Analysis", publisher = "Springer--Verlag", address = "New York", year = "1986", } @Article{Kirkpatrick83, author = "S.~Kirkpatrick and C.~D.~Gelatt Jr. and M.~P.~Vecchi", title = "Optimization by Simulated Annealing", journal = "Science", volume = "220", year = "1983", pages = "671--680", } @Article{Metropolis53, author = "N.~A.~Rosenbluth and M.~Rosenbluth and A.~Teller and E.~Teller", title = "Equation of State Calculations by Fast Computing Machines", journal = "Journal of Chemical Physics", volume = "21", number = "6", year = "1953", pages = "1087--1092", abstract = "the original simulated annealing ref" } @Article{Lippmann87, author = "R.~P.~Lippmann", title = "An Introduction to Computing with Neural Nets", pages = "4--22", journal = "IEEE ASSP Magazine", month = apr, year = "1987", } @Article{McCulloch43, author = "W.~S.~McCulloch and W.~Pitts", title = "A Logical Calculus of Ideas Immanent in Nervous Activity", journal = "Bulletin of Mathematical Biophysics", volume = "5", year = "1943", abstract = "These two were the first to introduce the neuronal unit but only with threshold or linear activation functions. There neural model could learn to calculate simple, linearly separable problems but could not suffice for the XOR problem where the training data is linearly inseparable. They started the hype of neural nets until Minsky and Papert realised the weakness of their model and burried the Connectionist movement at its birth. There were some politics involved as well, for example the DARPA reseacrh grants (symbolic AI or connectionism?).", } @Article{Mitchison89, author = "G.~J.~Mitchison and R.~M.~Durbin", title = "Bounds on the Learning Capacity of Some Multi-Layer Networks", pages = "345--356", journal = "Biological Cybernetics", volume = "60", year = "1989", } @Article{Packard80, author = "N.~H.~Packard and J.~PCrutchfield and J.~D.~Farmer and R.~S.~Shaw", title = "Geometry from a Time Series", pages = "712--716", journal = "Physical Review Letters", volume = "45", year = "1980", } @Book{Rosenblatt62, author = "F.~Rosenblatt", title = "Principles of Neurodynamics", publisher = "Spartan", address = "New York", year = "1962", } @Article{Rosenblatt, author = "F.~Rosenblatt", title = "The perceptron: a probabilistic model for information storage and organization in the brain", pages = "386--408", journal = "Psychological Review", volume = "65", } @Article{Calvin87, author = "W.~H.~Calvin", title = "The Brain as a Darwin Machine", journal = "Nature", pages = "33--34", address = "London", volume = "330", month = nov, year = "1987", abstract = "For parallel computers to simulate our brains, we must face the fact that human beings have a better claim on the title 'Homo seriatim' than 'Homo sapiens' - we're more consistently serial than wise.", } @Article{Varotsos84, author = "P.~Varotsos and K.~Alexopoulos", title = "Physical Properties of the Variation of the Electric Field of the Earth Preceding Earthquakes 1 and 2", journal = "Techtonophysics", volume = "110", year = "1984", abstract = "A paper for the seismic prediction work, from a signal processing point of view", } @Book{vonNeumann, author = "J.~von Neumann", title = "The Computer and the Brain", publisher = "Yale University Press", pages = "66--82", } @Book{Freeman91, author = "J.~Freeman", title = "Neural Networks: Theory and Practice", publisher = "Addison--Wesley", year = "1991", abstract = "Excellent reference for the theory of Neural Networks and a description of many different models. A nice introduction to the gradient-descent algorithm and Delta-rule for training of the Feed Forward Model. Special attention to the first three chapters.", } @Book{Wasserman89, author = "P.~D.~Wasserman", title = "Neural Computing: Theory and practice", publisher = "Van Nostrand Reinhold", year = "1989", abstract = "A quite simple book but none-the-less a good introduction.", } @Book{Bechtel90, author = "W.~Bechtel and A.~Abrahamsen", title = "Connectionism and the mind", publisher = "Basil Blackwell", year = "1990", } @Book{Anderson64, author = "A.~Anderson", title = "Minds and machines", publisher = "Prentice--Hall", year = "1964", } @Book{Arnheim69, author = "R.~Arnheim", title = "Visual thinking", publisher = "University of California Press", year = "1969", abstract = "Mostly psychology and theory of cognition with a philosophical perspective", } @Article{Kohonen82, author = "T.~Kohonen", title = "Self---organizing formation of topologically correct feature maps", journal = "Biological Cybernetics", volume = "43", pages = "59--69", year = "1982", } @Book{Kohonen84, author = "T.~Kohonen", title = "Self--Organization and Associative Memory", publisher = "Springer--Verlag", edition = "3rd", address = "Berlin", year = "1989", abstract = "Kohonen is considered as one of the pioneers in Self--Organisational models of neural networks.", } @Article{Hopfield84, author = "J.~J.~Hopfield", title = "Neurons with a graded response have collective computational properties like those of two--state neurons", journal = "Proceedings of the National Academy of Science USA", volume = "81", pages = "3088--3092", month = May, year = "1984", } @TechReport{Burrows:Niranjan93, author = "T.~L.~Burrows and M.~Niranjan", title = "The Use of Feed--forward and Recurrent Neural Networks for System Identification", type = "Technical Report", institution = "Cambridge University Engineering Department", year = "1993", } @Article{Cybenko89, author = "G.~Cybenko", title = "Approximation by superpositions of a sigmoidal function", journal = "Mathematics of Control, Signals, and Systems", publisher = "Springer--Verlag", volume = "2", number = "4", pages = "303--314", year = "1989", } @Article{White89, author = "H.~White", title = "Learning in artificial neural networks: {A} statistical perspective", journal = "Neural Computation", publisher = "MIT Press", volume = "1", number = "4", pages = "425--464", year = "1989", } @Article{Ackley85, author = "D.~H.~Ackley and G.~E.~Hinton and T.~J.~Sejnowski", title = "A Learning Algorithm for Boltzmann Machines", journal = "Cognitive Science", volume = "9", year = "1985", } @Article{Hinton89, author = "G.~E.~Hinton", title = "Connectionist learning procedures", journal = "Artificial Intelligence", volume = "40", pages = "185--234", year = "1989", } @Article{Fodor88, author = "J.~A.~Fodor and Z.~W.~Pylyshyn", title = "Connectionism and cognitive architecture: {A} critical analysis", journal = "Cognition", volume = "28", pages = "3--72", year = "1988", abstract = "Yet another publication critisizing the Connectionist approach to AI from a cognitive perspective this time.", } @Book{Lavine83, author = "R.~A.~Lavine", title = "Neurophysiology: The Fundamentals", publisher = "The Collamore Press", address = "Lexington, MA", year = "1983", abstract = "An introduction to the physiology of the brain.", } @Book{MacGregor87, author = "R.~J.~MacGregor", title = "Neural and Brain Modeling", publisher = "Academic Press", address = "San Diego, CA", year = "1987", abstract = "A text on how to model the brain and its functions.", } @Article{Yao93, author = "X.~Yao", title = "A Review of Evolutionary Artificial Neural Networks", journal = "International Journal of Intelligent Systems", volume = "8", pages = "539--567", year = "1993", abstract = "An excellent review of the current methods of applying genetic algorithms in neural networks in many dfferent ways, i.e. architecture, training and learning rules.", } @Book{Goldberg89, author = "D.~E.~Goldberg", title = "Genetic Algorithms in Search, Optimization, and Machine Learning", publisher = "Addison--Wesley", address = "Reading", year = "1989", abstract = "One of the best books on genetic algorithms. Very comprehensive.", } @Book{Reeves95, author = "R.~R.~Reeves", title = "Modern Heuristic Techniques for Combinatorial Problems", publisher = "McGraw--Hill", year = "1995", abstract = "A good and comprehensive review of the current trends in optimisation by non-exhaustive search." } @Book{Bishop95, author = "C.~Bishop", title = "Neural Networks for Pattern Recognition", publisher = "Clarendon Press", address = "Oxford", year = "1995", abstract = "A very nice book on neural networks, error minimisation and pattern recognition. Mention of entities in Chapter 9, page 364.", } @inproceedings{Krogh95, author = "Anders Krogh and Jesper Vedelsby", title = "Neural Network Ensembles, Cross Validation, and Active Learning", booktitle = "Advances in Neural Information Processing Systems", volume = "7", publisher = "The {MIT} Press", editor = "G. Tesauro and D. Touretzky and T. Leen", pages = "231--238", year = "1995", url = "citeseer.ist.psu.edu/krogh95neural.html", abstract = "Learning of continuous valued functions using neural network ensembles" } @article{Jacobs95, author = "R.~A.~Jacobs", title = "Methods for combining experts' probability assessment", journal = "Neural Computation", volume = "7", pages = "867--888", year = "1995", abstract = "proper overview of modular nn" } @article{Xu1992, author = "L.~Xu and A.~Krzyzak and C.~Y.~Suen", title = "Methods of combining multiple classifiers and their applications to handwriting recognition", journal = "EEE Transactions on Systems, Man, and Cybernetics", volume = "22", number = "3", pages = "418--435", year = "1992", abstract = "proper overview of modular nn" } @article{Sharkey96, author = "A.~Sharkey", title = "On combining artificial neural nets", journal = "Connection Science", volume = "8", pages = "299--313", year = "1996", abstract = "some review of modular nn", url = "citeseer.nj.nec.com/sharkey96combining.html" } @inproceedings{Sharkey98, author = "A.~Sharkey and N.~Sharkey and S.~Cross", title = "Adapting an Ensemble Approach for the Diagnosis of Breast Cancer", year = "1998", booktitle = "{ICANN}", publisher = "Springer-Verlag", pages = "281--286", abstract = "neural network ensembles in medicine, more", url = "citeseer.nj.nec.com/sharkey98adapting.html" } @article{Zhou2002, author = "Z.~H.~Zhou and Y.~Jiang, Y.~B.~Yang, S.~F.~Chen", title = "Lung Cancer Cell Identification Based on Artificial Neural Network Ensembles", journal = "Artificial Intelligence in Medicine, 2002, 24(1): 25-36", volume = "24", number = "1", pages = "25--36", year = "2002", absract = "neural network ensembles in medicine", url = "citeseer.nj.nec.com/zhou02lung.html" } @Article{Hansen1990, author = "L.~K.~Hansen and P.~Salamon", title = "Neural network ensembles", journal = "IEEE Transactions in Pattern Analysis and Machine Intelligence", volume = "12", number = "10", pages = "993--1001", year = "2002", } @Article{Wolpert92, author = "D.~H.~Wolpert", title = "Stacked generalisation", journal = "Neural Networks", volume = "5", pages = "241--259", year = "1992", abstract = "Mentioned by Bishop95 for using several neural networks to obtain an alternative to a single neural network.", } @Book{Weigend93, author = "A.~S.~Weigend", title = "Time Series Prediction : Forecasting the Future and Understanding the Past", publisher = "Addison--Wesley", year = "1993", abstract = "A case study of applications of neural networks in the field of time series prediction." } @Book{Gately96, author = "E.~Gately", title = "Neural Networks for Financial Forecasting,", publisher = "John Wiley", year = "1996", address = "New York", } @Article{Nielsen87, author = "R.~Hecht--Nielsen", title = "Kolmogorov's mapping neural network existence theorem", journal = "IEEE First International Conference on Neural Networks, San Diego", publisher = "SOS Printing", volume = "3", pages = "11-14", year = "1987", abstract = "Use this instead of kolmogorov's original (1957) because it is in russian!!!" } @Article{Kolmogorov57, author = "A.~N.~Kolmogorov", title = "On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition.", journal = "Doklady Akademii Nauk SSR", volume = "114", pages = "953--956", year = "1957", abstract = "Kolmogorov's original work" } @Article{Hilbert00, author = "D.~Hilbert", title = "Mathematical Problems", journal = "Bulletin of the American Mathematical Society", volume = "8", pages = "437--479", year = "1902", abstract = "Hilbert setting problems to mathematicians, this is a translation to english of the original lecture delivered by hilbert before the International Congress of Mathematicians at Paris, 1900. Problem 13 is the one related to neural networks. Solution by Kolmogorov and Lorenz(later?)." } @Article{Funahashi89, author = "K.~Funahashi", title = "On the approximate realization of continuous mappings by neural networks", journal = "Neural Networks", volume = "2", pages = "183--192", year = "1989", abstract = "Funahashi, Cybenko, Hornik - the trio" } @Article{Hornik91, author = "K.~Hornik", title = "Approximation capabilities of Multilayer Feedforward Networks", journal = "Neural Networks", volume = "4", pages = "251--257", year = "1991", abstract = "Funahashi, Cybenko, Hornik - the trio" } @Article{Gori:Tesi90, author = "M.~Gori and A.~Tesi", title = "Some Examples of Local Minima during Learning with Back-propagation", journal = "Parallel Architectures and Neural Networks", year = "1990", address = "Vietri sul Mare(IT)", abstract = "Back prop related" } @InCollection{Frasconi:Gori:Tesi, author = "P.~Frasconi and M.~Gori and A.~Tesi", title = "Susccesses and failures of backpropagation: a theoretical investigation", booktitle = "Progress in Neural Networks", editor = "O.~Omidvar", publisher = "Ablex Publishing", abstract = "Back prop related" } @Article{Brady:Raghavan:Slawny89, author = "M.~L.~Brady and R.~Raghavan and J.~Slawny", title = "Back-Propagation fails to Separate Where Perceptrons Succeed", journal = "IEEE Transactions on Circuits and Systems", year = "1989", volume = "36", pages = "665--674", abstract = "Back prop related" } @InCollection{Mozer:Smolensky89, author = "M.~C.~Mozer and P.~Smolensky", title = "Skeletonization: a technique for trimming the fat from a network via relevance assessment", booktitle = "Advances in Neural Information Processing Systems", editor = "D.~S.~Touretzky", publisher = "Morgan Kaufmann", address = "San Mateo, CA", volume = "1", year = "1989", pages = "107--115", abstract = "Pruning from bishop" } @InCollection{Hanson:Pratt89, author = "S.~J.~Hanson and L.~Y.~Pratt", title = "Comparing biases for minimal network construction with back-propagation", booktitle = "Advances in Neural Information Processing Systems", editor = "D.~S.~Touretzky", publisher = "Morgan Kaufmann", address = "San Mateo, CA", volume = "1", year = "1989", pages = "177--185", abstract = "Pruning from bishop" } @InCollection{Chauvin89, author = "Y.~Chauvin", title = "A back-propagation algorithm with optimal use of hidden units", booktitle = "Advances in Neural Information Processing Systems", editor = "D.~S.~Touretzky", publisher = "Morgan Kaufmann", address = "San Mateo, CA", volume = "1", year = "1989", pages = "519--526", abstract = "Pruning from bishop" } @Article{Prechelt96, author = "L.~Prechelt", title = "A Quantative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice", journal = "Neural Networks", volume = "9", year = "1996", abstract = "Mentioned by Flexer96 because it says that not many journal papers make experiments. crap", } @InProceedings{Flexer96, author = "A.~Flexer", title = "Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice", booktitle = "Cybernetics and Systems 1996, Proceedings of the $13^{th}$ European Meeting on Cybernetics and Systems Research", pages = "1005--1008", editor = "R.~Trappl", address = "Austrian Society for Cybernetic Studies", year = "1996", abstract = "the t-test is mentioned as a good test for statistical significance of neural nets results" } @InProceedings{Perrone94, author = "M.~P.~Perrone", title = "General averaging results for convex optimization", booktitle = "Proceedings 1993 Connectionist Models Summer School", pages = "364--371", editor = "M.~C.~Mozer", address = "Hillsdale, NJ", publisher = "Lawrence Erlbaum", year = "1994", abstract = "Averaging nns" } @Article{Perrone:Cooper93, author = "M.~P.~Perrone and L.~N.~Cooper", title = "When networks disagree: ensemble methods for hybrid neural networks", journal = "Artificial Neural Networks for Speech and Vision", editor = "R.~J.~Mammone", pages = "126--142", year = "1993", address = "London", publisher = "Chapman and Hall", abstract = "Averaging nns, a first paper, see also Bishop's book for nns and pattern recognition page 364" } @Article{Jacobs:Jordan:Nowlan:Hinton91, author = "R.~A.~Jacobs and M.~I.~Jordan and S.~J.~Nowlan and G.~E.~Hinton", title = "Adaptive mixture of local experts", journal = "Neural Computation", volume = "3", pages = "79--87", abstract = "gating nns", year = "1991" } @Book{Rudin64, author = "W.~Rudin", title = "Principles of Mathematical Analysis", publisher = "McGraw-Hill", address = "New York", year = "1964", } @Book{Bellman61, author = "R.~Bellman", title = "Adaptive Control Processes: A Guided Tour", publisher = "Princeton University Press", year = "1961", abstract = "The curse of dimensionality also in http://acrux.fmi.uni-passau.de/~sick/FAQ2.html#A_curse" } @Article{Levy:Montalvo85, author = "A.~V.~Levy and A.~Montalvo", title = "The Tunnelling Algorithm for the Global Minimization of Functions", journal = "SIAM J. Sci. Stat. Comput.", volume = "6", pages = "15--29", year = "1985", abstract = "the levy6 function for benchmark" } @Article{Wang:Hsu91, author = "S.~Wang and C.~HHsu", title = "Terminal attractor learning algorithms for backpropagation neural networks", journal = "International Joint Conference on Neural Networks", place = "Singapore", pages = "183--189", year = "1991", month = nov, publisher = "IEEE Press", abstract = "avoid local minima suggestions" } @Article{Yu92, author = "X.~Yu", title = "Can backpropagation error surface not have local minima?", journal = "IEEE Transactions on Neural Networks", pages = "1019--1020", year = "1992", volume = "3", number = "6", abstract = "avoid local minima with as many hidden units as required" } @Article{Dybowski96, author = "R.~Dybowski and P.~Weller and R.~Chang and V.~Gant", title = "Prediction of outcome in critically ill patients using artificial neural networks synthesised by genetic algorithm", journal = "The Lancet", volume = "347", pages = "1146--50", year = "1996" } @Article{Chiu93, author = "C.-T.~Chiu and K.~Mehrotra and C.~Mohan and S.~Ranka", title = "Robustness of Feedforward Neural Networks", journal = "Second IEEE International Conf. on Neural Networks", volume = "2", pages = "783--788", month = mar, year = "1993" } @TechReport{McKelvey92, author = "T.~McKelvey", title = "Neural networks applied to optimal flight control", type = "Technical Report", institution = "Link$\ddot{o}$ping University, Sweeden", year = "1992", } @TechReport{Musen96, author = "L.~Ohno--Machado and M.~A.~Musen", title = "Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction", type = "Technical Report", institution = "Knowledge Systems Laboratory, Medical Computer Science, Stanford University", year = "1996", month = feb } @Article{Papik98, author = "K.~Papik and B.~Molnar and R.~Schaefer and Z.~Dombovari and Z.~Tulassay and J.Feher", title = "Application of neural networks in medicine - a review", journal = "Medical Science Monitor", year = "1998", volume = "4", number = "3", pages = "538--546", url = "http://medscimonit.com/pub/vol_4/no_3/694.pdf" } @Article{Cenys88, author = "A.~Cenys and K.~Pyragas", title = "Estimation of the Number of Degrees of Freedom from Chaotic Time Series", journal = "Physics Letters A", year = "1988", volume = "129", number = "4", pages = "227--230", month = "May", abstract = "A method for the chaotic time series analysis is proposed. It allows one to determine the number of degrees of freedom involved in oscillations from a single observable. The method has been verified for some known stochastic models." } @Article{Pi94, author = "H.~Pi and C.~Peterson", title = "Finding the Embedding Dimension and Variable Dependencies in Time Series", journal = "Neural Computation", year = "1994", volume = "6", pages = "509--520", abstract = "We present a general method, the delta-test, which establishes functional dependencies given a sequence of measurements. The approach is based on calculating conditional probabilities from vector component distances. Imposing the requirement of continuity of the underlying function, the obtained values of the conditional probabilities carry information on the embedding dimension and variable dependencies. The power of the method is illustrated on synthetic time-series with different time-lag dependencies and noise levels and on the sunspot data. The virtue of the method for preprocessing data in the context of feedforward neural networks is demonstrated. Also, its applicability for tracking residual errors in output units is stressed." } @article{JordanJacobs94, author = "M.~I.~Jordan and R.~A.~Jacobs", title="Hierarchical Mixtures of Experts and the EM Algorithm", year="1994", journal="Neural Computation", volume=6, number="2", pages="181--214", abstract = "We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain." } @article{JacobsJordanNowlanHinton91, author = "R.~A.~Jacobs and M.~I.~Jordan and S.~J.~Nowlan and G.~E.~Hinton", title="Adaptive Mixtures of Local Experts", journal="Neural Computation", volume="3", number="1", year=1991, pages="79-87", abstract = "We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link between these two apparently different approaches. We demonstrate that the learning procedure divides up a vowel discrimination task into appropriate subtasks, each of which can be solved by a very simple expert network." } @incollection{JordanJacobs92, author = "M.~I.~Jordan and R.~A.~Jacobs", title="Hierarchies of Adaptive Experts", year="1992", booktitle="NIPS", volume= 4, editor="R.~P.~Lippmann and J.~Moody and D.~S.~Touretzky", publisher="Morgan Kaufmann", pages="985-992" } @article{JacobsJordanBarto91, author = "R.~A.~Jacobs and M.~I.~Jordan and A.~G.~Barto", title="Task Decomposition through competition in a modular connectionist architecture: The what and where vision tasks.", journal="Cognitive Science", year=1991 } @inproceedings{WaterhouseRobinson94, author = "S.~R.~Waterhouse and A.~J.~Robinson", title="Classification using Hierarchical Mixtures of Experts", booktitle="IEEE Workshop on Neural Networks for Signal Processing", year=1994, pages="177-186" } @inproceedings{XuHintonJordan95, author = "Lei Xu and Geoff Hinton and Michael I.~Jordan", title ="An alternative model for mixtures of experts", year = 1994, booktitle= "NIPS", volume = 7, editor="G.~Tesauro and D.~S.~Touretzky and T.~K.~Leen", publisher="MIT Press" } @Article{Baxt90, author = "W.~G.~Baxt", title = "Use of an artificial neural network for data analysis in clinical decision-making: The diagnosis of acute coronary occlusion", journal = "Neural Computation", pages = "480--490", year = "1990", volume = "2", } @Article{Baxt94, author = "W.~G.~Baxt", title = "Complexity, chaos and human physiology: the justification for non-linear neural computational analysis", journal = "Cancer Let (IRELAND)", pages = "85--93", year = "1994", volume = "77", } @Article{Rogers94, author = "S.~K.~Rogers and D.~W.~Ruck and M.~Kabrisky", title = "Artificial neural networks for early detection and diagnosis of cancer", journal = "Cancer Let (IRELAND)", pages = "79--83", year = "1994", volume = "77", month = mar } @Article{Doig93, author = "G.~S.~Doig and K.~J.~Inman and W.~J.~Sibbald and C.~M.~Martin and J.~M.~Robertson", title = "Modeling mortality in the intensive care unit: comparing the performance of a back-propagation, associative-learning neural network with multivariate logistic regression", journal = "Proc Annu Symp Comput Appl Med Care", pages = "361--5", year = "1993" } @Article{Baxt91, author = "W.~G.~Baxt", title = "Use of an artificial neural network for the diagnosis of myocardial infarction", journal = "Ann Intern Med", pages = "843--8", year = "1991", month = dec, volume = 115, number = 11 } @Article{SapsII, author = "J.~R.~le Gall and S.~Lemeshow and F.~Saulnier", title = "A new simplified acute physiology score (SAPS II) based on a European/North American multicentre study", journal = "JAMA", pages = "2957--63", year = "1993", volume = 270 } @Article{ApacheII, author = "W.~E.~Knaus and E.~A.~Draper and D.~P.~Wagner and J.~E.~Zimmerman", title = "APACHE II: a severity of disease classification system", journal = "Crit Care Med", pages = "818--29", year = "1985", volume = 13 } @Article{ApacheIII, author = "W.~E.~Knaus and E.~A.~Draper and D.~P.~Wagner and J.~E.~Zimmerman", title = "The APACHE III prognostic system risk prediction of hospital mortality for critically ill hospitalized patients", journal = "Chest", pages = "1619--36", year = "1991", volume = 10 } @Article{Chang88, author = "R.~W.~S.~Chang and S.~Jacobs and B.~Lee", title = "Predicting outcome among intensive care unit patients using computerised trend analysis of daily Apache II scores corrected for organ system failure", journal = "Int Care Med", pages = "558--566", year = "1988", volume = 14, publisher = "Springer--Verlag", abstract = "critique of the logistic regression techniques employed by usual severity-of-illness scoring systems such as ApacheII." } @Article{Reggia93, author = "J.~A.~Reggia", title = "Neural computation in medicine", journal = "Artificial Intelligence in Medicine", pages = "143--157", volume = 5, number = 2, year = "1993", abstract = "The use of neural networks in the diagnoses of diseases" } @Article{Burke94, author = "H.~B.~Burke", title = "Artificial neural networks for cancer research: Outcome prediction", journal = "Seminars in Surgical Oncology", volume = 10, pages = "73--9", year = 1994, abstract = "The use of neural networks in the prediction of outcome in tumor stage in oncology" } @Article{McGonigal93, author = "M.~D.~McGonigal and J.~Cole and C.~W.~Schwab and D.~R.~Kauder and M.~F.~Rotondo and P.~B.~Angood", title = "A new approach to probability of survival scoring for trauma quality assurance", journal = "Journal of Trauma", volume = 34, number = 6, pages = "863--8", year = "1993", abstract = "The use of neural networks in the prediction of outcome after trauma" } @Article{Ravdin:Clarke92, author = "P.~M.~Ravdin and G.~M.~Clarke", title = "A practical application of neural network analysis for predicting outcome of individual breast cancer patients", journal = "Breast Cancer Research and Treatment", volume = 22, number = 3, year = "1992", pages = "285--93", } @inproceedings{Schank76, author = "R.~C.~Schank", title = "The role of memory in language processing", year = 1976, booktitle= "The structure of human memory", pages = "162--189", editor="C.~N.~Cofer", address = "San Fransisco", publisher="Freeman", abstract = "scripts" } @inproceedings{Minsky75, author = "M.~Minsky", title = "A framework for representing knowledge", year = 1975, booktitle= "The psychology of computer vision", pages = "211--277", editor="P.~H.~Winston", address = "New York", publisher="McGraw-Hill", abstract = "frames" } @inproceedings{Rumelhart75, author = "D.~E.~Rumelhart", title = "Notes on a schema for stories", year = 1975, booktitle= "Representation and understanding", pages = "211--236", editor="D.~G.~Bobrow and A.~Collins", address = "New York", publisher="Academic Press", abstract = "schemata" } @Article{Newell80, author = "A.~Newell", title = "Physical Symbol Systems", journal = "Cognitive Science", volume = 4, year = 1980, pages = "135--183", abstract = "The Physical Symbol System Hypothesis" } @Article{Minsky90, author = "M.~Minsky", title = "Logical vs. Analogical or Symbolic vs. Connectionist or Neat vs. Scruffy", journal = "Artificial Intelligence at MIT, Expanding Frontiers", volume = 1, editor = "P.~H.~Winston", year = 1990, publisher = "MIT Press", abstract = "Minsky's defecation about connectionism and symbolic AI" } @Book{Moody93, author = "T.~C.~Moody", title = "Philosophy and Artificial Intelligence", publisher = "Prentice Hall", year = 1993, abstract = "world acclaimed definitions in AI" } @Book{Papert88, author = "S.~Papert", title = "One AI or Many?", publisher = "Daedalus", year = 1988, abstract = "the snow white tale..." } @Article{Penny95, author = "S.~Penny", title = "The Darwin Machine: Artificial Life and Interactive Art", journal = "New Formations, UK", year = 1996, location = "UK", presented = "Fifth Biennale of Art and Technology", abstract = "It has a few interesting bits about reductionism and emergence e.g `Emergence throws reductionism in the trash' and quotes DeLanda92, see `http://www-art.cfa.cmu.edu/www-penny/texts/Darwin_Machine_.html'" } @Article{DeLanda92, author = "M.~DeLanda", title = "Virtual Environments as Intuition Synthesisers", journal = "Cultural diversity in the global village", year = 1992, publisher = "TISEA Catalog, Australian Network for Art and Technology", abstract = "If the properties of matter and energy at any given level of organisation cannot be explained by the properties of the underlying levels, it follows that biology cannot be reduced to physics or anthropology to biology" } @Book{Descartes, author = "Descartes", title = "Discours de la M{\'{e}}thode", year = 1637, abstract = "Descartes talks about the 4 principles of the scientific method, rules 2 and 3 talk about reductionism" } @Article{Sprecher65, author = "D.~A.~Sprecher", title = "On the structure of continuous functions of several variables", journal = "Trans. Am. Math. Soc.", year = 1965, month = mar, volume = 115, pages = "340--355", abstract = "An improvement over kolmogorov's 1957 theorem" } @Article{PoggioGirosi89, author = "T.~Poggio and F.~Girosi", title = "A theory of Networks for Approximation and Learning", journal = "MIT AI Memo No.~1140", year = 1989, month = jul, abstract = "{\em $g$ is at least as complex , in terms of bits needed to represent it, as $f$}, kolmogorov is irrelevant in a few words." } @Article{Kurkova91, author = "V.~K{\accent '27 u}rkov{\'a}", title = "Kolmogorov's theorem is relevant", journal = "Neural Computation", year = 1991, volume = "3", pages = "617--622", abstract = "kolmogorov is or is not relevant(poggio)???? for fuck's sake" } @Article{Geman92, author = "S.~Geman and E.~Bienenstock", title = "Neural networks and the bias-variance dilemma", journal = "Neural Computation", volume = 4, year = 1992, pages = "1--58", abstract = "The bias-variance tradeoff or overfitting" } @Article{Vapnik_etal97, author = "V.~N.~Vapnik and S.~Golowich and A.~Smola", title = "Support vector method for function approximation, regression, estimation and signal processing", journal = "Advances in Neural Information Processing Systems", volume = 9, editor = "M.~Mozer and M.~Jordan and T.~Petsche", year = 1997, address = "Cambridge, MA", publisher = "MIT Press", pages = "281--287", abstract = "Support Vector Machines at ATT Bell Labs" } @Article{VapnikLerner63, author = "V.~N.~Vapnik and A.~Lerner", title = "Pattern recognition using generalised portrait method", journal = "Automation and Remote Control", year = 1963, volume = "24", abstract = "The first attempt to Support Vector Machine in the USSR" } @Article{VapnikChervonenkis64, author = "V.~N.~Vapnik and A.~Ya.~Chervonenkis", title = "A note on one class of perceptrons", journal = "Automation and Remote Control", year = 1964, volume = "25", abstract = "follow-up of VapnikLerner63" } @Article{Vapnik71, author = "V.~N.~Vapnik and A.~Ya.~Chervonenkis", title = "On the uniform convergence of relative frequencies of events to their probabilities", journal = "Theory of Probability and its Applications", year = 1971, volume = "16", number = "2", pages = "264--280", abstract = "the VC dimension" } @Book{VapnikChervonenkis74, author = "V.~N.~Vapnik and A.~Ya.~Chervonenkis", title = "Theory of Pattern Recognition [in Russian]", publisher = "Nauka", year = 1974, address = "USSR", abstract = "Basis of vapnik's work" } @Book{Vapnik79, author = "V.~N.~Vapnik", title = "Estimation of Dependences Based on Empirical Data [in Russian]", publisher = "Nauka", year = 1979, address = "USSR", abstract = "more basis, english translation by Springer Verlag, NY, 1982" } @Book{Vapnik95, author = "V.~N.~Vapnik", title = "The Nature of Statistical Learning Theory", publisher = "Springer", year = 1995, address = "New York", abstract = "more basis" } @Book{Burges98, author = "C.~J.~C.~Burges", title = "A tutorial on Support Vector Machines for Pattern Recognition", publisher = "Kluwer Academic Publishers", year = 1998, address = "Boston", abstract = "a good tutorial on SVM -- mentions numerical precision as a problem for QP solvers" } @inproceedings{Blumer86, author = "A.~Blumer and A.~Ehrenfeucht and D.~Haussler and M.~Warmuth", title = "Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension", year = 1, booktitle= "Proceedings of the $18^{th}$ Annual ACM Symposium on the Theory of Computing", address = "New York", publisher="The Association for Computing Machinery", abstract = "introduction of the VC dimension to computational theory" } @Article{Blumer87, author = "A.~Blumer and A.~Ehrenfeucht and D.~Haussler and M.~Warmuth", title = "Occam's Razor", journal = "Information Processing Letters", year = 1987, volume = "24", pages = "377--380", abstract = "post vapnik2" } @Article{Blumer89, author = "A.~Blumer and A.~Ehrenfeucht and D.~Haussler and M.~Warmuth", title = "Learnability and the Vapnik-Chervonenkis Dimension", journal = "Journal of the ACM", year = 1989, volume = "36", number = "4", pages = "929--965", abstract = "post vapnik2" } @Article{Baum89, author = "E.~B.~Baum and D.~Haussler", title = "What size net gives valid generalization?", journal = "Neural Computation", year = 1989, volume = "1", number = "1", pages = "151--160", abstract = "upper bounds on sample length using VC dim, very very pessimistic!" } @Article{Judd88, author = "J.~S.~Judd", title = "On the complexity of learning shallow neural networks", journal = "Journal of Complexity", year = 1988, volume = "4", pages = "177--192", abstract = "computational complexity issues of neural network training" } vapni@Book{Garey79, author = "M.~R.~Garey and D.~Johnson", title = "Computers and Intractability: A Guide to the theory of NP-completeness", publisher = "W.H.~Freeman", year = 1979, address = "San Fransisco", abstract = "a reference book on complexity classes" } @Book{Judd90, author = "J.~S.~Judd", title = "Neural Network Design and the Complexity of Learning", publisher = "MIT Press", year = 1990, address = "Cambridge, MA", abstract = "Judd's phd thesis made a book, on the computational complexity of learning in neural nets" } @Inproceedings{Blum88, author = "A.~Blum and R.~Rivest", title = "Training a 3-node neural network is NP-complete", booktitle = "Proceedings of the $1^{st}$ Workshop on Computational Learning Theory", year = 1988, publisher = "Morgan-Kaufmann", abstract = "computational complexity issues of neural network training" } @Article{Sima94, author = "J.~$\check{S}$ima", title = "Loading Deep Networks is Hard", journal = "Neural Computation", year = 1994, volume = "6", pages = "842--850", abstract = "computational complexity issues of neural network training" } @Article{Sima96, author = "J.~$\check{S}$ima", title = "Back-propagation is not efficient", journal = "Neural Networks", year = 1996, volume = "6", abstract = "computational complexity issues of neural network training" } 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