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Understanding Machine Learning
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Algorithmic Learning Theory
This book constitutes the refereed proceedings of the 13th International Conference on Algorithmic Learning Theory, ALT 2002, held in Lübeck, Germany in November 2002. The 26 revised full papers presented together with 5 invited contributions and an introduction were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on learning Boolean functions, boosting and margin-based learning, learning with queries, learning and information extraction, inductive inference, inductive logic programming, language learning, statistical learning, and applications and heuristics.
Theory and Applications of Models of Computation
TAMC 2006 was the third conference in the series. The previous two meetings were held May 17–19, 2004 in Beijing, and May 17–20, 2005 in Kunming
Advances in Neural Information Processing Systems 13
The proceedings of the 2000 Neural Information Processing Systems (NIPS) Conference.The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2000 conference.
Advances in Cryptology - CRYPTO '89
CRYPTO is a conference devoted to all aspects of cryptologic research. It is held each year at the University of California at Santa Barbara. Annual meetings on this topic also take place in Europe and are regularly published in this Lecture Notes series under the name of EUROCRYPT. This volume presents the proceedings of the ninth CRYPTO meeting. The papers are organized into sections with the following themes: Why is cryptography harder than it looks?, pseudo-randomness and sequences, cryptanalysis and implementation, signature and authentication, threshold schemes and key management, key distribution and network security, fast computation, odds and ends, zero-knowledge and oblivious transfer, multiparty computation.
Data Classification
Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlyi
Towards a More Comprehensive Theory of Learning in Computers
Abstract: "We attempt to determine the theoretical boundaries of the ability of computers to learn. We consider several rigorous models of learning, aimed at addressing types of learning problems excluded from earlier models. In Part I, we consider learning dependencies between real-valued quantities in situations where the environment is assumed to be an adversary, operating within constraints that model the prior knowledge of the learner. While our assumptions as to the form of these dependencies is taken from previous work in statistics, this work is distinguished by the fact that the analysis is worst case. In Part II, we consider learning in situations in which the learner's environment is assumed to be at least partially random. We consider methods for extending the tools for learning [0,1]-valued functions to apply to the learning of many-valued and real-valued functions. We also study the learning of [0,1]-valued functions in situations in which the relationship to be learned is gradually changing as learning is taking place."
