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The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
About the Author
Alexander J. Smola is a Postdoctoral Fellow at GMD-FIRST, Berlin. Peter L. Bartlett is Senior Fellow, Computer Sciences Laboratory, Australian National University. Bernhard Schölkopf is a Researcher at Microsoft Research Ltd., Cambridge, UK. Dale Schuurmans is Assistant Professor of Computer Science at the University of Waterloo, Ontario, Canada.
The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
About the Author
Alexander J. Smola is a Postdoctoral Fellow at GMD-FIRST, Berlin. Peter L. Bartlett is Senior Fellow, Computer Sciences Laboratory, Australian National University. Bernhard Schölkopf is a Researcher at Microsoft Research Ltd., Cambridge, UK. Dale Schuurmans is Assistant Professor of Computer Science at the University of Waterloo, Ontario, Canada.
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Inside This Book
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First Sentence:
The goal is to find some decision function g : RN {-1, 1} that accurately predicts the labels of unseen data points (x, y). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
maximal margin perceptron, mean field algorithm, conventional support vector machine, billiard algorithm, drawn training sets, linear programming machines, margin support vectors, adaptive margin, fat shattering dimension, postal database, kernel adatron, margin algorithm, kernel classifiers, weak learner, ordinal regression, quadratic programming formulation, label noise, regularization networks, combined hypotheses, misclassified examples, rank boundaries, large margin classifiers, perceptron algorithm, base classifiers, generalization error Key Phrases - Capitalized Phrases (CAPs): (learn more)
Robust Ensemble Learning, Adult Quadratic, Simple Classification Problem, West Dayton Street Madison, Australia Alex, Carleton Street, Generalized Approximate Cross Validation, George House, Guildhall Street Cambridge, Proof Let New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
The goal is to find some decision function g : RN {-1, 1} that accurately predicts the labels of unseen data points (x, y). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
maximal margin perceptron, mean field algorithm, conventional support vector machine, billiard algorithm, drawn training sets, linear programming machines, margin support vectors, adaptive margin, fat shattering dimension, postal database, kernel adatron, margin algorithm, kernel classifiers, weak learner, ordinal regression, quadratic programming formulation, label noise, regularization networks, combined hypotheses, misclassified examples, rank boundaries, large margin classifiers, perceptron algorithm, base classifiers, generalization error Key Phrases - Capitalized Phrases (CAPs): (learn more)
Robust Ensemble Learning, Adult Quadratic, Simple Classification Problem, West Dayton Street Madison, Australia Alex, Carleton Street, Generalized Approximate Cross Validation, George House, Guildhall Street Cambridge, Proof Let New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Citations (learn more)
This book cites 33
books:
See all 33 books this book cites
2
books
cite this book:
- Statistical Learning Theory by Vladimir N. Vapnik on 4 pages
- Advances in Neural Information Processing Systems 12 by Sara A. Solla in Front Matter, Back Matter (1), and Back Matter (2)
- Theory of reproducing kernels and its applications (Pitman research notes in mathematics series) by Saburou Saitoh in Back Matter
- Interval Orders and Interval Graphs: Study of Partially Ordered Sets (Wiley-Interscience Series in Discrete Mathematics) by Peter C. Fishburn in Back Matter
- Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics) by Ludwig Fahrmeir in Back Matter
See all 33 books this book cites
- Machine Learning and Data Mining for Computer Security: Methods and Applications (Advanced Information and Knowledge Processing) by Marcus A. Maloof in Back Matter
- Pattern Recognition: 27th DAGM Symposium, Vienna, Austria, August 31 - September 2, 2005, Proceedings (Lecture Notes in Computer Science / Image Processing, ... Vision, Pattern Recognition, and Graphics) by Walter Kropatsch on page 84
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