Informace o publikaci
Adaptive kernel matching pursuit for pattern classification
| Autoři | |
|---|---|
| Rok publikování | 2004 |
| Druh | Článek ve sborníku |
| Konference | PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND APPLICATIONS, VOLS 1AND 2 |
| Citace | |
| Klíčová slova | pattern recognition; kernel matching pursuit; sparse classifiers |
| Popis | A sparse classifier is guaranteed to generalize better than a denser one, given they perform identical on the training set. However, methods like Support Vector Machine, even if they produce relatively sparse models, are known to scale linearly as the number of training examples increases. A recent proposed method, the Kernel Matching Pursuit, presents a number of advantages over the SVM, like sparser solutions and faster training. In this paper we present an extension of the KMP in which we prove that adapting the dictionary to the data results in improved performances. We discuss different techniques for dictionary adaptation and present some results on standard datasets. |