We extend Support Vector Machines to input spaces that are sets by ensuring that the classifier is invariant to permutations of subelements within each input. Such permutations in...
Many applications in text and speech processing require the analysis of distributions of variable-length sequences. We recently introduced a general kernel framework, rational ker...
In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examp...
Cross-domain learning methods have shown promising
results by leveraging labeled patterns from auxiliary domains
to learn a robust classifier for target domain, which
has a limi...
Dong Xu, Ivor Wai-Hung Tsang, Lixin Duan, Stephen ...
We examine methods for constructing regression ensembles based on a linear program (LP). The ensemble regression function consists of linear combinations of base hypotheses generat...