The Elements of Statistical Learning

Trevor Hastie Robert Tibshirani Jerome Friedman

About this book

Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines.

Subjects

006.3/1
006.3'1 22
artificial intelligence
artificial intelligence (incl. robotics)
bioinformatics
biology
biology--data processing
computational biology
computational biology--methods
computational intelligence
computer appl. in life sciences
computer science
database management
data interpretation, statistical
data mining
data processing
electronic data processing
forecasting
future studies
general
inference
intelligence (ai) & semantics
logic
machine learning
machine theory
mathematical computing
mathematical statistics
mathematics & statistics -> mathematics -> probability
methodology
methods
probability and statistics in computer science
professional, career & trade -> computer science -> database management
professional, career & trade -> computer science -> intelligence (ai) & semantics
professional, career & trade -> computer science -> machine theory
professional, career & trade -> computer science -> special topics
q325.75 .h37 2001
q325.75 .h37 2009
sci18030
scm27004
scs11001
social sciences -> philosophy -> logic & critical reasoning
social sciences -> social sciences -> future studies
statistical data interpretation
statistical theory and methods
statistics
statistics as topic
statistics as topic--methods
statistics for engineering, physics, computer science, chemistry and earth sciences
suco11649
supervised learning (machine learning)