Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.
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Inside This Book
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First Sentence:
Pattern classification is the field devoted to the study of methods designed to categorize data into distinct classes. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
exponential concentration inequalities, online gradient descent, expectation propagation, multiclass problems, concentration inequality, belief propagation, junction tree, marginal likelihood, factor graphs, bounded differences, statistical learning theory, gradient descent algorithm, empirical risk, machine learning research, sample representation, threshold unit, bounded random variables, union bound Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, New York, Neural Information Processing Systems, Morgan Kaufmann, Neural Computation, Annals of Probability, Cambridge University Press, Springer-Verlag Berlin Heidelberg, Springer Verlag, San Francisco, Academic Press, John Wiley, San Mateo, Journal of Combinatorial Theory, Oxford University Press, Connectionist Models Summer School, Journal of the American Statistical Association New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Pattern classification is the field devoted to the study of methods designed to categorize data into distinct classes. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
exponential concentration inequalities, online gradient descent, expectation propagation, multiclass problems, concentration inequality, belief propagation, junction tree, marginal likelihood, factor graphs, bounded differences, statistical learning theory, gradient descent algorithm, empirical risk, machine learning research, sample representation, threshold unit, bounded random variables, union bound Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, New York, Neural Information Processing Systems, Morgan Kaufmann, Neural Computation, Annals of Probability, Cambridge University Press, Springer-Verlag Berlin Heidelberg, Springer Verlag, San Francisco, Academic Press, John Wiley, San Mateo, Journal of Combinatorial Theory, Oxford University Press, Connectionist Models Summer School, Journal of the American Statistical Association New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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This book cites 58
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- Statistical Learning Theory by Vladimir N. Vapnik on 4 pages
- Learning in Graphical Models (Adaptive Computation and Machine Learning) by Michael I. Jordan on page 40, page 71, and page 111
- The Nature of Statistical Learning Theory (Information Science and Statistics) by Vladimir Vapnik on page 167, page 204, and Back Matter
- Probabilistic Methods for Algorithmic Discrete Mathematics (Algorithms and Combinatorics) by Michel Habib on page 206, and Back Matter
- Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) by R. M. Dudley on page 205, and Back Matter
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