"Statistical Theory and Methods." . . "Statistical Theory and Methods" . "Data Mining and Knowledge Discovery." . . "Data Mining and Knowledge Discovery" . "Statistiques." . . "Exploration de données." . . "Mathematical Computing." . . "Artificial intelligence." . . "Data mining" . . "Data mining." . "Data Mining." . "Data Mining" . "Anvendt statistik" . . "Wiskundige statistiek." . . "Aprendizaje supervisado (Aprendizaje automático)" . . "Lernen." . . "Lernen (Informatik)" . . "Uczenie się automatyczne." . . "Maschinelles Lernen." . . "Maschinelles Lernen" . "Künstliche Intelligenz." . . "Datenanalyse." . . "Apprentissage supervisé (Intelligence artificielle)" . . "Prognoses." . . "Supervised learning." . . "Mineração de dados." . . "prévision statistique manuel." . . "Estatística." . . . "The elements of statistical learning : data mining, inference, and prediction"@en . . "The elements of statistical learning : data mining, inference, and prediction" . . . . . . . . . . . . . . . . "统计学习基础" . . . . . . . . . "Ressource Internet (Descripteur de forme)" . . . . . . . . . . . . . "Livre électronique (Descripteur de forme)" . . . . . . . . . . . . . . "The elements of statistical learning : data mining, inference and prediction" . . . . "Tong ji xue xi ji chu" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "The Elements of statistical learning : data mining, inference, and prediction" . . "The Elements of Statistical Learning : Data Mining, Inference, and Prediction" . . . . . . . . . . "During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for \"wide\" data (p bigger than n), including multiple testing and false discovery rates." . "Electronic books"@en . "Electronic books" . "During the past decade there has been an explosion in computation and information technology.; With it has come a vast amount of data in a variety of fields such as medicine, biology, finance, and marketing.; The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics.; Many of these tools have common underpinnings but are often expressed with different terminology.; This book describes the important ideas in these areas in a common conceptual framework.; While the approach is statistical, the emphasis is on concepts rather than mathematics." . "During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting, the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for wide data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting." . . . . . "The elements of statistical learning Data mining, inference, and prediction" . . . . . . . . "Livres électroniques" . . . . . . . . "The Elements of Statistical Learning Data Mining, Inference, and Prediction"@en . "The Elements of Statistical Learning Data Mining, Inference, and Prediction" . . . . . . . . . . . . . "During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting."@en . . . . . . . "The elements of statistical learning : data mining, inference, and prediction : Trevor Hastie, Robert Tibshirani, Jerome Friedman" . . . . . . "Matériel didactique" . . . . "The elements of statistical learning data mining, inference, and prediction" . "The elements of statistical learning data mining, inference, and prediction"@en . . . . . . . . . . . . . . . . . . . "The elements of statistical learning, second edition : data mining, inference, and prediction" . . . . . . . . . . . . . . . . . . . "Estadística matemática." . . "COMPUTERS Database Management Data Mining." . . "Computer Science IT." . . "Estatística computacional." . . "Statistics." . . "Computer Appl. in Life Sciences." . . "Computer Appl. in Life Sciences" . "Statistik Maschinelles Lernen." . . "Aprenentatge automàtic." . . "Aprenentatge automàtic" . "Artificial Intelligence (incl. Robotics)." . . "Artificial Intelligence (incl. Robotics)" . "Bioinformatics." . . "Springer Science+Business Media." . . "Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences" . . "Lernen Statistik Datenanalyse Data Mining." . . "Machine learning." . . "Machine-learning." . "données statistiques prévision recherche de l'information [manuel]" . . "Computational Biology/Bioinformatics." . . "Computational Biology/Bioinformatics" . "Statistik." . . "Statistik" . "Previsión, Teoría de la" . . "Inferência estatística." . . "exploration de données manuel." . . "données statistiques exploration de données prévision [manuel]" . . "Statystyka." . . "Estadística matemática Aprendizaje." . . "Artificiële intelligentie. Robotica. Simulatie. Graphics." . . "Apprendimento meccanico." . . "Mineria de dades." . . "Mineria de dades" . "Data warehouse" . . "Statistics as Topic." . . "Regressionsanalyser" . . "Computational Biology." . . "Apprentissage automatique." . . "Hastie, T." . . . . "Aprenentatge estadístic." . . "Supervised learning (Machine learning)" . . "Supervised learning (Machine learning)." . "Biology Data processing." . . "Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences." . . "Wissensextraktion." . . "Aprendizaje automático (Inteligencia artificial)" . . "Datamining." . . "Mathematical statistics." . . "Intel·ligència artificial." . . "Intel·ligència artificial" . "inférence statistique manuel." . . "Intelligence artificielle." . . "Tanulás irányítása." . . "Aprendizaje automático" . . "Mathematical statistics Knowledge and learning." . .