New Results on Error Correcting Output Codes of Kernel Machines  

Oleh:

Passerini, A. ; Pontil, M. ; Frasconi, P.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 1 (Jan. 2004), page 45-54.
Topik: kernel estimator; new results; error correcting; output codes; kernel machines

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.10 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We study the problem of multiclass classification within the framework of error correcting output codes (ECOC) using margin - based binary classifiers. Specifically, we address two important open problems in this context: decoding and model selection. The decoding problem concerns how to map the outputs of the classifiers into class codewords. In this paper we introduce a new decoding function that combines the margins through an estimate of their class conditional probabilities. Concerning model selection, we present new theoretical results bounding the leave - one - out (LOO) error of ECOC of kernel machines, which can be used to tune kernel hyperparameters. We report experiments using support vector machines as the base binary classifiers, showing the advantage of the proposed decoding function over other functions of I he margin commonly used in practice. Moreover, our empirical evaluations on model selection indicate that the bound leads to good estimates of kernel parameters.

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