Model selection in machine learning with applications to document clustering

Model selection in machine learning with applications to document clustering

A objective function based on a Bayesian statistical estimation framework is used to determine an optimal model selection by choosing both the optimal number of clusters and the optimal feature set. Heuristics can be applied to find the optimal (or at least sub-optimal) of this objective function in...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: DOM BYRON EDWARD, VAITHYANATHAN SHIVAKUMAR
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
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Beschreibung
Zusammenfassung:A objective function based on a Bayesian statistical estimation framework is used to determine an optimal model selection by choosing both the optimal number of clusters and the optimal feature set. Heuristics can be applied to find the optimal (or at least sub-optimal) of this objective function in terms of the feature sets and the number of clusters, wherein the maximization of the objective function corresponds to the optimal model structure.