An Elementary Introduction to Statistical Learning Theory by Sanjeev Kulkarni
* Serves as a fundamental introduction to statistical learning theory and its role in understanding human learning and inductive reasoning. * Topics of coverage include: probability, pattern recognition, optimal Bayes decision rule, nearest neighbor rule, kernel rules, neural networks, and support vector machines.-
Introductory Statistics for Business and Economics
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Applied Longitudinal Analysis
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Applied Econometric Times Series
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Sample Size Determination and Power
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Statistical Methods for Survival Data Analysis
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Bayesian Analysis for the Social Sciences
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The Statistical Analysis of Failure Time Data
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Regression Models for Time Series Analysis
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Statistical Rules of Thumb
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Bayesian Theory
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Graphical Models in Applied Multivariate Statistics
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Loss Models
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Introduction to Linear Regression Analysis
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Aspects of Multivariate Statistical Theory
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Fundamentals of Queueing Theory
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Statistics of Extremes
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Applied Econometric Time Series
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Structural Equations with Latent Variables
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Applied Linear Regression
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Directional Statistics
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An Introduction to Probability Theory and Its Applications, Volume 2
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System Reliability Theory
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An Introduction to Categorical Data Analysis
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Markov Decision Processes
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Applied Logistic Regression
“The main focus of the book is on the ideas behind basic principles of learning theory and I can strongly recommend the book to anyone who wants to comprehend these ideas” (Mathematical Reviews, 1 January 2013)
“It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.” (Zentralblatt MATH, 2012)
SANJEEV KULKARNI, PhD, is Professor in the Department of Electrical Engineering at Princeton University, where he is also an affiliated faculty member in the Department of Operations Research and Financial Engineering and the Department of Philosophy. Dr. Kulkarni has published widely on statistical pattern recognition, nonparametric estimation, machine learning, information theory, and other areas. A Fellow of the IEEE, he was awarded Princeton University's President's Award for Distinguished Teaching in 2007.
GILBERT HARMAN, PhD, is James S. McDonnell Distinguished University Professor in the Department of Philosophy at Princeton University. A Fellow of the Cognitive Science Society, he is the author of more than fifty published articles in his areas of research interest, which include ethics, statistical learning theory, psychology of reasoning, and logic.
| SKU | Unavailable |
| ISBN 13 | 9780470641835 |
| ISBN 10 | 0470641835 |
| Title | An Elementary Introduction to Statistical Learning Theory |
| Author | Sanjeev Kulkarni |
| Series | Wiley Series In Probability And Statistics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Wiley |
| Year published | 2011-08-02 |
| Number of pages | 232 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |