Neural and Statistical Classifiers-Taxonomy and Two Case Studies  

Oleh:

Oja, E. ; Koistinen, P. ; Holmstrom, L. ; Laaksonen, J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 1 (1997), page 5-17.
Topik: TAXONOMY; neural; statistical classifiers; taxonomy

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Pattern classification using neural networks and statistical methods is discussed. We give a tutorial overview in which popular classifiers are grouped into distinct categories according to their underlying mathematical principles ; also, we assess what makes a classifier neural. The overview is complemented by two case studies using handwritten digit and phoneme data that test the performance of a number of most typical neural - network and statistical classifiers. Four methods of our own are included : reduced kernel discriminant analysis, the learning k - nearest neighbors classifier, the averaged learning subspace method, and a version of kernel discriminant analysis.

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