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