We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities...
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operat...
Structural alignments are the most widely used tools for comparing proteins with low sequence similarity. The main contribution of this paper is to derive various kernels on prote...
Multirelational classification aims at discovering useful patterns across multiple inter-connected tables (relations) in a relational database. Many traditional learning techniques...