A Solution Manual for ESL
Ex. 11.7 (TODO)
Initializing search
YuhangZhou88/ESL_Solution
Home
ESL Solution
A Solution Manual for ESL
YuhangZhou88/ESL_Solution
Home
ESL Solution
ESL Solution
2 Overview of Supervised Learning
2 Overview of Supervised Learning
Ex. 2.1
Ex. 2.2
Ex. 2.3
Ex. 2.4
Ex. 2.5
Ex. 2.6
Ex. 2.7
Ex. 2.8
Ex. 2.9
3 Linear Methods for Regression
3 Linear Methods for Regression
Ex. 3.1
Ex. 3.2
Ex. 3.3
Ex. 3.4
Ex. 3.5
Ex. 3.6
Ex. 3.7
Ex. 3.8
Ex. 3.9
Ex. 3.10
Ex. 3.11
Ex. 3.12
Ex. 3.13
Ex. 3.14
Ex. 3.15
Ex. 3.16
Ex. 3.17
Ex. 3.18
Ex. 3.19
Ex. 3.20
Ex. 3.21
Ex. 3.22
Ex. 3.23
Ex. 3.24
Ex. 3.25
Ex. 3.26
Ex. 3.27
Ex. 3.28
Ex. 3.29
Ex. 3.30
4 Linear Methods for Classification
4 Linear Methods for Classification
Ex. 4.1
Ex. 4.2
Ex. 4.3
Ex. 4.4
Ex. 4.5
Ex. 4.6
Ex. 4.7
Ex. 4.8
Ex. 4.9
5 Basis Expansions and Regularization
5 Basis Expansions and Regularization
Ex. 5.1
Ex. 5.2
Ex. 5.3
Ex. 5.4
Ex. 5.5
Ex. 5.6
Ex. 5.7
Ex. 5.8
Ex. 5.9
Ex. 5.10
Ex. 5.11
Ex. 5.12
Ex. 5.13
Ex. 5.14
Ex. 5.15
Ex. 5.16
Ex. 5.17
Ex. 5.18
Ex. 5.19
6 Kernel Smoothing Methods
6 Kernel Smoothing Methods
Ex. 6.1
Ex. 6.2
Ex. 6.3
Ex. 6.4
Ex. 6.5
Ex. 6.6
Ex. 6.7
Ex. 6.8
Ex. 6.9
Ex. 6.10
Ex. 6.11
Ex. 6.12
7 Model Assessment and Selection
7 Model Assessment and Selection
Ex. 7.1
Ex. 7.2
Ex. 7.3
Ex. 7.4
Ex. 7.5
Ex. 7.6
Ex. 7.7
Ex. 7.8
Ex. 7.9
Ex. 7.10 (TODO)
8 Model Inference and Averaging
8 Model Inference and Averaging
Ex. 8.1
Ex. 8.2
Ex. 8.3
Ex. 8.4
Ex. 8.5
Ex. 8.6
Ex. 8.7
9 Additive Models and Trees
9 Additive Models and Trees
Ex. 9.1
Ex. 9.2
Ex. 9.3
Ex. 9.4
Ex. 9.5
Ex. 9.6
10 Boosting and Additive Trees
10 Boosting and Additive Trees
Ex. 10.1
Ex. 10.2
Ex. 10.3
Ex. 10.4
Ex. 10.5
Ex. 10.6
Ex. 10.7
Ex. 10.8
Ex. 10.9
Ex. 10.10
Ex. 10.11
Ex. 10.12
11 Neural Networks
11 Neural Networks
Ex. 11.1
Ex. 11.2
Ex. 11.3
Ex. 11.4
Ex. 11.5
Ex. 11.6 (TODO)
Ex. 11.7 (TODO)
12 Flexible Discriminants
12 Flexible Discriminants
Ex. 12.1
Ex. 12.2
Ex. 12.3
Ex. 12.4
Ex. 12.5 (TODO)
Ex. 12.6
Ex. 12.7
Ex. 12.8
Ex. 12.9
Ex. 12.10
Ex. 12.11
13 Prototypes and Nearest Neighbors
13 Prototypes and Nearest Neighbors
Ex. 13.1
Ex. 13.2
Ex. 13.3
Ex. 13.4
Ex. 13.5
Ex. 13.6
Ex. 13.7
Ex. 13.8 (TODO)
14 Unsupervised Learning
14 Unsupervised Learning
Ex. 14.1
Ex. 14.2
Ex. 14.7
Ex. 14.8
Ex. 14.10
Ex. 14.11
Ex. 14.18
Ex. 14.19
Ex. 14.20
Ex. 14.23
Ex. 14.24
15 Random Forests
15 Random Forests
Ex. 15.1
Ex. 15.2 (TODO)
Ex. 15.3
Ex. 15.4
Ex. 15.5
Ex. 15.6
Ex. 15.7
16 Ensemble Learning
16 Ensemble Learning
Ex. 16.1
Ex. 16.2
Ex. 16.3
Ex. 16.4 (TODO)
17 Undirected Graphical Models
17 Undirected Graphical Models
Ex. 17.1
Ex. 17.2
Ex. 17.3
Ex. 17.4
Ex. 17.5
Ex. 17.6
Ex. 17.7
Ex. 17.8
Ex. 17.9
Ex. 17.10
Ex. 17.11
Ex. 17.12
18 High Dimensional Problems
18 High Dimensional Problems
Ex. 18.1
Ex. 18.2
Ex. 18.3
Ex. 18.4
Ex. 18.5
Ex. 18.6
Ex. 18.7
Ex. 18.8
Ex. 18.9
Ex. 18.10
Ex. 18.11
Ex. 18.12
Ex. 18.13
Ex. 18.14
Ex. 18.15
Ex. 18.16
Ex. 18.17
Ex. 18.18
Ex. 18.19
Ex. 18.20
Ex. 11.7 (TODO)
TODO