M
Martin Anthony
Researcher at London School of Economics and Political Science
Publications - 102
Citations - 3898
Martin Anthony is an academic researcher from London School of Economics and Political Science. The author has contributed to research in topics: Artificial neural network & Boolean function. The author has an hindex of 22, co-authored 102 publications receiving 3610 citations. Previous affiliations of Martin Anthony include Royal Holloway, University of London & University of London.
Papers
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Book
Neural Network Learning: Theoretical Foundations
Martin Anthony,Peter L. Bartlett +1 more
TL;DR: The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction, and discuss the computational complexity of neural network learning.
Journal ArticleDOI
Structural risk minimization over data-dependent hierarchies
TL;DR: A result is presented that allows one to trade off errors on the training sample against improved generalization performance, and a more general result in terms of "luckiness" functions, which provides a quite general way for exploiting serendipitous simplicity in observed data to obtain better prediction accuracy from small training sets.
Book
Computational learning theory: an introduction
Martin Anthony,Norman Biggs +1 more
TL;DR: This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included, and will form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
Journal ArticleDOI
A result of Vapnik with applications
Martin Anthony,John Shawe-Taylor +1 more
TL;DR: A new proof of a result due to Vapnik is given, and its implications for the theory of PAC learnability are discussed, with particular reference to the learnability of functions taking values in a countable set.
Proceedings ArticleDOI
A framework for structural risk minimisation
TL;DR: The paper introduces a framework for studying structural risk minimisation in a PAC context and considers the more general case when the hierarchy of classes is chosen in response to the data.