The maximum-margin approach to learning text classifiers: methods theory, and algorithms
نویسنده
چکیده
Diese Dissertation entwickelt und erforscht einen neuen Ansatz zum Lernen von Textklassifikationsregeln aus Beispielen. Der Ansatz stützt sich auf die Einsicht, dass bei der Textklassifikation nicht die Anzahl der Attribute die Schwierigkeit einer Lernaufgabe bestimmt, sondern dass dimensionsunahängige Komplexitätsmaße notwendig sind. Die Dissertation zeigt den Zusammenhang dieser Maße mit den statistischen Eigenschaften von Text, deren Umsetzung in effektiven und praktikablen Methoden zur Textklassifikation und ihre Implementierung in effizienten Algorithmen.
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