| Publisher | Hong Kong University of Science and Technology | ||
|---|---|---|---|
| Format | 451.7KB PDF | Date added | 08 Jun 2003 |
| Topics | Software Engineering, Online - Distance Education, Learning Management Systems | ||
| Downloads | 12 | ||
Distance-based methods in machine learning and pattern recognition have to rely on a metric distance between points in the input space. Instead of specifying a metric a priori, the authors seek to learn the metric from data via kernel methods and Multidimensional Scaling (MDS) techniques. Under the classification setting, authors define discriminant kernels on the joint space of input and output spaces and present a specific family of discriminant kernels. This family of discriminant kernels is attractive because the induced metrics are Euclidean and Fisher separable, and MDS techniques can be used to find the low-dimensional Euclidean representations (also called feature vectors) of the induced metrics.
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