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Recent Advances in Predictive (Machine) Learning.

DE2004826695

Publication Date	2004
Personal Author	Friedman, J. H.
Page Count	16
Abstract	Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.
Keywords	Artificial intelligence Machine learning Computers Forecasting Learning Prediction rules Decision tree analysis Predictive learning Support vector machines Boosted decision trees
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Stanford Linear Accelerator Center, CA.; Department of Energy, Washington, DC.
Supplemental Notes	Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200507

Recent Advances in Predictive (Machine) Learning.

Recent Advances in Predictive (Machine) Learning.
DE2004826695

Publication Date	2004
Personal Author	Friedman, J. H.
Page Count	16
Abstract	Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.
Keywords	Artificial intelligence Machine learning Computers Forecasting Learning Prediction rules Decision tree analysis Predictive learning Support vector machines Boosted decision trees
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Stanford Linear Accelerator Center, CA.; Department of Energy, Washington, DC.
Supplemental Notes	Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200507