The elements of statistical learning

Description:

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: Supervised learning (Machine learning),  Database management,  Artificial Intelligence (incl. Robotics),  Statistics,  Artificial intelligence,  Biology,  Data processing,  Computer science,  Mathematical statistics,  Computer Appl. in Life Sciences,  Probability and Statistics in Computer Science,  Statistical Theory and Methods,  Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences,  Computational biology--methods,  Statistics as topic--methods,  Data interpretation, statistical,  Q325.75 .h37 2001,  006.3/1,  Computational Biology,  Methods,  Statistics as Topic,  Statistical Data Interpretation,  Data mining,  Bioinformatics,  Machine learning,  Inference,  Forecasting,  Computational intelligence,  Methodology,  Electronic data processing,  Biology--data processing,  Mathematical computing,  Q325.75 .h37 2009,  006.3'1 22,  Intelligence (ai) & semantics,  Machine theory,  Logic,  Future studies,  General,  Professional, career & trade -> computer science -> intelligence (ai) & semantics,  Professional, career & trade -> computer science -> database management,  Professional, career & trade -> computer science -> machine theory,  Social sciences -> philosophy -> logic & critical reasoning,  Social sciences -> social sciences -> future studies,  Mathematics & statistics -> mathematics -> probability,  Professional, career & trade -> computer science -> special topics,  Scm27004,  Sci18030,  Scs11001,  Suco11649,  Sci23050,  Scl17004,  Sci21017,  Biological sciences & nutrition -> biology -> life sciences general,  Professional, career & trade -> computer science -> computer science,  Sci21000,  2970,  3820,  2923,  3921,  2912