We introduce a new approach to the training of classifiers for performance on multiple tasks. The proposed hybrid training method leads to improved generalization via a better low-dimensional representation of the problem space. The quality of the representation is assessed by embedding it in a two-dimensional space using multi-dimensional scaling, allowing a direct visualization of the results. The performance of the approach is demonstrated on a highly non-linear image classification task. (PsycINFO Database Record (c) 2003 APA ) (journal abstract)
CITATION STYLE
Intrator, N., & Edelman, S. (1996). Making a Low-Dimensional Representation Suitable for Diverse Tasks. In Learning to Learn (pp. 135–157). Springer US. https://doi.org/10.1007/978-1-4615-5529-2_6
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