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