해법 - solution [수학의 응용과 빅데이터] The Elements of Statistical Learning, Da…
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Download : 솔루션 - solution [수학의.pdf
12. Support Vector Machines and
순서
Google page rank algorithm, a
direct approach to ICA
10. Boosting and Additive Trees New example from ecology; some
8. Model Inference and Averaging
sparse PCA, non-negative matrix
2. Overview of Supervised Learning
설명
해법 - solution [수학의 응용과 빅데이터] The Elements of Statistical Learning, Data Mining, Inference, Second
7. Model Assessment and Selection Strengths and pitfalls of crossvalidation
솔루션 - solution [수학의 응용과 빅데이터] The Elements of Statistical Learning, Data Mining, Inference, Second Edition 저자 - Hastie, Trevor, Tibshirani, Robert, Friedman, 출판사 - Jerome - Springer
3. Linear Methods for Regression LAR algorithm and generalizations
9. Additive Models, Trees, and
출판사 - Jerome - Springer
6. Kernel Smoothing Methods
factorization archetypal analysis,
Additional illustrations of RKHS
레포트 > 기타
17. Undirected Graphical Models New
2003 challenge
material split off to Chapter 16.
solution - solution [수학의 응용과 빅데이터] The Elements of Statistical Learning, Data Mining, Inference, Second Edition - Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome - Springer
해법 - solution [수학의 응용과 빅데이터] The Elements of Statistical Learning, Data Mining, Inference, Second Edition
of the lasso
Chapter What’s new
16. Ensemble Learning New
Nearest-Neighbors
11. Neural Networks Bayesian neural nets and the NIPS
5. Basis Expansions and Regularization
저자 - Hastie, Trevor, Tibshirani, Robert, Friedman,
1. Introduction
Related Methods
Flexible Discriminants
13. Prototype Methods and
14. Unsupervised Learning Spectral clustering, kernel PCA,
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솔루션 - solution [수학의 응용과 빅데이터] The Elements of Statistical Learning, Data Mining, Inference, Second
Path algorithm for SVM classifier
nonlinear dimension reduction,
목 차
15. Random Forests New
4. Linear Methods for Classification Lasso path for logistic regression
18. High-Dimensional Problems New
다.
