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Review: The Elements of Statistical Learning: Data Mining, Inference, and PredictionUser Review - David - GoodreadsGreat book covering the principles of applied statistical learning. The book's mathematical rigor is semi-formal, opting for intuitive explanations and keeping proofs to a minimum. Chapters contain a thorough treatment of their subject, touching on modern research topics. Read full review Review: The Elements of Statistical Learning: Data Mining, Inference, and PredictionUser Review - Clif Davis - GoodreadsExcellent book. Has repaid multiple rereadings and is a wonderful springboard for developing your own ideas in the area. Currently I'm going through Additive Models again which I breezed by the first ... Read full review Related books
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Common terms and phrasesA'-means AdaBoost additive model algorithm approximation average B-spline basis functions bias boosting bootstrap Chapter classification clustering coefficients computed criterion cross-validation curve data points dataset decision boundary defined density dimension discriminant analysis dissimilarity distribution error rate estimate example feature space Gaussian hence hyperplane input iteration kernel knots least squares left panel linear discriminant analysis linear model linear regression log-likelihood logistic regression loss function maximum likelihood methods minimize mixture model nearest-neighbor neighborhood neighbors neural network node nonlinear observations optimal orthogonal output panel of Figure panel shows parameters penalty plot polynomial posterior prediction error predictors principal components principal curves problem procedure projection pursuit prototypes quadratic response ridge regression right panel rule Section shrinkage smoothing spline solution spam split squared error statistical subset subspace supervised learning support vector test error tion training data training error training set tree values variables variance VC dimension wavelet zero Popular passagesPage 513 - Experiments with a new boosting algorithm. Machine Learning: Proceedings of the Thirteenth International Conference, Morgan Kauffman, San Francisco, pp. Page 513 - Proceedings of the Ninth Annual Conference on Computational Learning Theory, pp. 325-332. Freund, Y. and Schapire, R. (1997). A decision-theoretic generalization of online learning and an application to boosting, Journal of Computer and System Sciences 55: 119-139. Page 516 - Another interpretation of the EM algorithm for mixture distributions. Page 514 - Gelman, A., Carlin, J., Stern, H., and Rubin, D. (1995). Bayesian Data Analysis. London: Chapman & Hall. Page 517 - Kohonen, T. (1989). Self-Organization and Associative Memory (3rd edition), Springer- Verlag, Berlin. Page 509 - AR ( 1993 | Universal approximation bounds for superpositions of a sigmoid function. IEEE Transactions on Information Theory 39. Page 509 - JA and Rosenfeld, E. (eds) (1988). Neurocomputing: Foundations of Research. MIT Press : Cambridge, MA. Page 518 - Madigan, D. and Raftery, A. (1994), Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window. References to this bookFrom other books
From Google ScholarLeast Angle RegressionBradley Efron, Trevor Hastie, Iain Johnstone, Robert Tibshirani - 2004 - Annals of Statistics r8s: inferring absolute rates of molecular evolution and ...Michael J Sanderson - 2003 - Bioinformatics Novel methods improve prediction of species’ distributions from ...Jane Elith, Catherine H Graham, Robert P Anderson, Miroslav Dudık Simon Ferrier, Antoine Guisan ... - 2006 - ECOGRAPHY Regularization and variable selection via the elastic netHui Zou, Trevor Hastie References from web pagesElements of Statistical Learning: data mining, inference, and ... The elements of statistical learning: Data mining, inference, and ... The Elements of Statistical Learning, Data Mining, Inference, and ... citeulike: The Elements of Statistical Learning Free Downloads: The Elements of Statistical Learning by T. Hastie ... The Elements of Statistical Learning The Elements of Statistical Learning: Data Mining, Inference, and ... The Elements of Statistical Learning: Data Mining, Inference, and ... Reading Group on The Elements of Statistical Learning Bibliography Machine Learning The Elements of Statistical Learning ... Bibliographic information
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