This volume contains 17 of the contributed papers presented at the 1st European Conference on Computational Learning Theory. Also included are invited presentations on the complexity of learning on neural nets, on new directions in computational learning theory, and on a neurodial model for cognitive functions. The proceedings give an overview of current work in computational learning theory, ranging from results inspired by neural network research to those arising from more classical artificial intelligence approaches. The study of machine learning within the mathematical framework of complexity theory has been a relatively recent development. The burgeoning interest in the application of machine learning to a wide variety of problems from control to financial market prediction has fired a corresponding upsurge in mathematical research.
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."..clean, readable, self-contained treatment of the foundations of PAC learnability theory." R. Roos, Computing Reviews ."..notable for its clean, readable, self-contained treatment of the foundations of PAC learnability theory." R. Roos, Artificial Intelligence ."..a welcome addition to the limited range of literature on computational learning theory, and it should perform a useful service in alerting a wider audience to this interesting and lively area..." Mathematical reviews
This is a self contained volume in which the authors concentrate on the 'probably approximately correct model'. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
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New! Hard bound in rust cloth with bright silver titles, first edition, xi + Pp239. A new, unread copy without a dust jacket. 560 grams. A volume from The Institute of Mathematics & Its Applications Conference Series; New Series, Number 53. Seller Inventory # 5421
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