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Understanding Machine Learning
monograph
Author(s):
Shai Shalev-Shwartz
,
Shai Ben-David
Publication date
(Online):
2009
Publisher:
Cambridge University Press
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Annual Reviews AI, Machine Learning, and Society
Author and book information
Book
ISBN:
9781107298019
Publication date (Print):
2014
Publication date (Online):
2009
DOI:
10.1017/CBO9781107298019
SO-VID:
284d5be5-51c6-4b4a-a087-ad304f18d195
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Book chapters
pp. xv
Preface
pp. 1
Introduction
pp. 13
A Gentle Start
pp. 22
A Formal Learning Model
pp. 31
Learning via Uniform Convergence
pp. 36
The Bias-Complexity Trade-off
pp. 43
The VC-Dimension
pp. 58
Nonuniform Learnability
pp. 73
The Runtime of Learning
pp. 89
Linear Predictors
pp. 101
Boosting
pp. 114
Model Selection and Validation
pp. 124
Convex Learning Problems
pp. 137
Regularization and Stability
pp. 150
Stochastic Gradient Descent
pp. 167
Support Vector Machines
pp. 179
Kernel Methods
pp. 190
Multiclass, Ranking, and Complex Prediction Problems
pp. 212
Decision Trees
pp. 219
Nearest Neighbor
pp. 228
Neural Networks
pp. 245
Online Learning
pp. 264
Clustering
pp. 278
Dimensionality Reduction
pp. 295
Generative Models
pp. 309
Feature Selection and Generation
pp. 325
Rademacher Complexities
pp. 337
Covering Numbers
pp. 341
Proof of the Fundamental Theorem of Learning Theory
pp. 351
Multiclass Learnability
pp. 359
Compression Bounds
pp. 364
PAC-Bayes
pp. 369
Technical Lemmas
pp. 372
Measure Concentration
pp. 380
Linear Algebra
pp. 385
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