The Elements of Statistical Learning Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome H. Friedman - WordSea
4.3
The Elements of Statistical Learning Data Mining, Inference, and Prediction
by Trevor Hastie, Robert Tibshirani, Jerome H. Friedman
"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"--
Computers & TechnologyComputer ScienceAI & Machine LearningDatabases & Big DataData MiningScience & Math
RELEASED2009
PUBLISHERSpringer
LENGTH745
LANGUAGEEN
4.3
The Elements of Statistical Learning Data Mining, Inference, and Prediction
by Trevor Hastie, Robert Tibshirani, Jerome H. Friedman
"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"--