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