K-Winner Machines for Pattern Classification  

Oleh:

Rovetta, S. ; Ridella, S. ; Zunino, R.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 2 (2001), page 371-385.
Topik: Pattern; k - winner machines; pattern classification

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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The paper describes the K - winner machine (KWM) model for classification. KWM training uses unsupervised vector quantization and subsequent calibration to label data - space partitions. A K - winner classifier seeks the largest set of best - matching prototypes agreeing on a test pattern, and provides a local - level measure of confidence. A theoretical analysis characterizes the growth function of a K - winner classifier, and the result leads to tight bounds to generalization performance. The method proves suitable for high - dimensional multiclass problems with large amounts of data. Experimental results on both a synthetic and a real domain (NIST handwritten numerals) confirm the approach effectiveness and the consistency of the theoretical framework.

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