Sets of approximating functions with finite Vapnik–Chervonenkis dimension for nearest-neighbors algorithms

► Reformulation of k-NN algorithm to alpha-NN ∗ algorithm (where alpha is a fraction). ► Establishing sets of functions for alpha-NN ∗, with finite capacity. ► Pointing out degrees of freedom for these sets. ► Proving theorems about dichotomies and VC-dimension for the proposed sets. According to a...

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Veröffentlicht in:	Pattern recognition letters Jg. 32; H. 14; S. 1882 - 1893
Hauptverfasser:	KLCSK, P, KORZEN, M
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 15.10.2011 Elsevier
Schlagworte:	Applied sciences Complexity selection Exact sciences and technology Generalization Information, signal and communications theory k-Nearest neighbors Signal and communications theory Signal representation. Spectral analysis Signal, noise Statistical learning theory Structural risk minimization Telecommunications and information theory Vapnik–Chervonenkis dimension Statistical learning theory k-Nearest neighbors Vapnik–Chervonenkis dimension Generalization Complexity selection Structural risk minimization Learning Nearest neighbour Vapnik-Chervonenkis dimension Cross validation Algorithm Signal analysis
ISSN:	0167-8655, 1872-7344
Online-Zugang:	Volltext
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