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Similarity-Based Classifiers: Problems and Solutions

Similarity-Based Classifiers: Problems and Solutions

This video was recorded at Machine Learning Summer School (MLSS), Chicago 2009. Similarity-based learning assumes one is given similarities between samples to learn from, and can be considered a special case of graph-based learning where the graph is given and fully-connected. Such problems arise frequently in computer vision, bioinformatics, and problems involving human judgment. We will review the field of similarity-based classification and describe the main problems encountered in adapting standard algorithms for this problem, including different approaches to approximating indefinite similarities by kernels. We will motivate why local methods lessen the indefinite similarity problem, and show that a kernelized linear interpolation and local kernel ridge regression can be profitably... Show More


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