Predicting Structured Objects with Support Vector Machines.

Machine Learning today offers a broad repertoire of methods for classification and regression. But what if we need to predict complex objects like trees, orderings, or alignments? Such problems arise naturally in natural language processing, search engines, and bioinformatics. The following explores...

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Detalles Bibliográficos
Publicado en:	Communications of the ACM Vol. 52; no. 11; pp. 97 - 105
Autores principales:	Joachims, Thorsten, Hofmann, Thomas, Yisong Yue, Chun-Nam Yu
Formato:	Artículo
Publicado:	Association for Computing Machinery Nov2009
Materias:	Support vector machines Machine learning Prediction models Natural language processing Algorithms Search engine programming
Acceso en línea:	Ver este registro en EBSCOhost

Descripción
Sumario:	Machine Learning today offers a broad repertoire of methods for classification and regression. But what if we need to predict complex objects like trees, orderings, or alignments? Such problems arise naturally in natural language processing, search engines, and bioinformatics. The following explores a generalization of Support Vector Machines (SVMs) for such complex prediction problems.