Anzeige
Erschienen in:
2008 | OriginalPaper | Buchkapitel
Data Complexity Analysis: Linkage between Context and Solution in Classification
verfasst von : Tin Kam Ho
Erschienen in: Structural, Syntactic, and Statistical Pattern Recognition
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
For a classification problem that is implicitly represented by a training data set, analysis of data complexity provides a linkage between context and solution. Instead of directly optimizing classification accuracy by tuning the learning algorithms, one may seek changes in the data sources and feature transformations to simplify the data geometry. Simplified class geometry benefits learning in a way common to many methods. We review some early results in data complexity analysis, compare these to recent advances in manifold learning, and suggest directions for further research.