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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.

Statistiekwetenschap --- Wiskundige statistiek --- Statistische fysica --- Moleculaire biologie --- Biologie --- Ingenieurswetenschappen. Technologie --- Programmering --- Informatiesystemen --- Artificiële intelligentie. Robotica. Simulatie. Graphics --- Computer. Informatica. Automatisering --- statistische kwaliteitscontrole --- industriële statistieken --- biologie --- informatica --- database management --- robots --- moleculaire biologie --- statistisch onderzoek --- Bioinformatics. --- Computational intelligence. --- Data mining. --- Forecasting. --- Inference. --- Machine learning. --- Statistics --- Supervised learning (Machine learning). --- Computerintelligentie. --- Statistiek --- Methodology. --- Methodologie. --- MACHINE LEARNING -- 516 --- STATISTICAL LEARNING -- 516 --- SUPERVISED LEARNING -- 516 --- Bioinformatics --- Data mining --- Forecasting --- Inference --- Machine learning --- 519.23 --- 519.2 --- 681.3*I26 --- Learning, Machine --- Artificial intelligence --- Machine theory --- Ampliative induction --- Induction, Ampliative --- Inference (Logic) --- Reasoning --- Forecasts --- Futurology --- Prediction --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Intelligence, Computational --- Soft computing --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- 519.23 Statistical analysis. Inference methods --- Statistical analysis. Inference methods --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Methodology --- Data processing --- Machine Learning --- Computational intelligence --- Statistical methods --- Supervised learning (Machine learning) --- Apprentissage supervisé (Intelligence artificielle) --- EPUB-LIV-FT LIVMATHE LIVSTATI SPRINGER-B --- Mathematical statistics --- Artificial intelligence. Robotics. Simulation. Graphics --- Statistique mathématique --- Artificial intelligence. --- Probabilities. --- Statistics . --- Bioinformatics . --- Computational biology . --- Artificial Intelligence. --- Data Mining and Knowledge Discovery. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Computational Biology/Bioinformatics. --- Computer Appl. in Life Sciences. --- Statistical analysis --- Statistical data --- Statistical science --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Mathematical statistics. --- Statistique mathématique --- Statistical decision. --- Statistics - Methodology --- Statistics. --- Computational biology.