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Books like Statistical learning theory by Vladimir Naumovich Vapnik


First publish date: 1998
Authors: Vladimir Naumovich Vapnik
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Statistical learning theory by Vladimir Naumovich Vapnik

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Books similar to Statistical learning theory (8 similar books)

The Elements of Statistical Learning by 12158869|Trevor Hastie book cover

📘 The Elements of Statistical Learning
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The Elements of Statistical Learning explains key statistical methods that underlie modern machine learning, data mining, and bioinformatics, covering supervised and unsupervised learning techniques such as classification trees, neural networks, and support vector machines.

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📘 KERNEL METHODS FOR PATTERN ANALYSIS
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Probably Approximately Correct Learnability Theory (The Stanford Computer Science Video Journal by 8157366|David Haussler book cover

📘 Probably Approximately Correct Learnability Theory (The Stanford Computer Science Video Journal
 by David Haussler

Provides a model of how effective behavior can be learned even in a world as complex as our own, shedding new light on human nature.

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📘 Learning and Soft Computing
 by Vojislav Kecman


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Pattern Recognition and Machine Learning by 8824929|Christopher M. Bishop book cover

📘 Pattern Recognition and Machine Learning
 by Christopher M. Bishop


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An Introduction to Statistical Learning by 7184749|Gareth James book cover

📘 An Introduction to Statistical Learning
 by Gareth James

An Introduction to Statistical Learning offers a practitioner‑friendly guide to modern statistical learning methods, including linear regression, classification, resampling, shrinkage, tree‑based models, SVMs, and clustering, with R tutorials and real‑world examples.

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Machine learning by 7953593|John Robert Anderson book cover

📘 Machine learning
 by John Robert Anderson


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Some Other Similar Books

Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
Support Vector Machines and Other Kernel-Based Learning Methods by Bernhard Schölkopf, Alexander J. Smola
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond by Bernhard Schölkopf, Alexander J. Smola
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