The number of functional errors escaping design verification and being released into final silicon is growing, due to the increasing complexity and shrinking production schedules ...
Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major te...
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chir...
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world ap...
Graph clustering (also called graph partitioning) -- clustering the nodes of a graph -- is an important problem in diverse data mining applications. Traditional approaches involve...