Understanding Machine Learning: From Theory to Algorithms/
Shalev-Shwartz, Shai
Understanding Machine Learning: From Theory to Algorithms/ Shai Shalev-Shwartz, Shai Ben-David - 1st ed. - New Delhi: Cambridge University Press, 2014 - xvi, 397 p. : ill. ; 26 cm.
Introduction --
Part I. Foundations --
2 A gentle start --
3 A formal learning model --
4 Learning via uniform convergence --
5 The bias-complexity tradeoff --
6 The VC-dimension --
7 Nonuniform learnability --
8 The runtime of learning --
Part II. From Theory to Algorithms --
9 Linear predictors --
10 Boosting --
11 Model selection and validation --
12 Convex learning problems --
13 Regularization and stability --
14 Stochastic gradient descent --
15 Support vector machines --
16 Kernel methods --
17 Multiclass, ranking, and complex prediction problems --
18 Decision trees --
19 Nearest neighbor --
20 Neural networks --
Part III. Additional Learning Models --
21 Online learning --
22 Clustering --
23Dimensionality reduction --
24 Generative models --
25 Feature selection and generation --
Part IV. Advanced Theory --
26 Rademacher complexities --
27 Covering numbers --
28 Proof of the fundamental theorem of learning theory --
29 Multiclass learnability --
30 Compression bounds --
31 PAC-Bayes.
9781107512825
Machine Learning
Algorithms
Computers
Understanding Machine Learning: From Theory to Algorithms/ Shai Shalev-Shwartz, Shai Ben-David - 1st ed. - New Delhi: Cambridge University Press, 2014 - xvi, 397 p. : ill. ; 26 cm.
Introduction --
Part I. Foundations --
2 A gentle start --
3 A formal learning model --
4 Learning via uniform convergence --
5 The bias-complexity tradeoff --
6 The VC-dimension --
7 Nonuniform learnability --
8 The runtime of learning --
Part II. From Theory to Algorithms --
9 Linear predictors --
10 Boosting --
11 Model selection and validation --
12 Convex learning problems --
13 Regularization and stability --
14 Stochastic gradient descent --
15 Support vector machines --
16 Kernel methods --
17 Multiclass, ranking, and complex prediction problems --
18 Decision trees --
19 Nearest neighbor --
20 Neural networks --
Part III. Additional Learning Models --
21 Online learning --
22 Clustering --
23Dimensionality reduction --
24 Generative models --
25 Feature selection and generation --
Part IV. Advanced Theory --
26 Rademacher complexities --
27 Covering numbers --
28 Proof of the fundamental theorem of learning theory --
29 Multiclass learnability --
30 Compression bounds --
31 PAC-Bayes.
9781107512825
Machine Learning
Algorithms
Computers