A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
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 ...
The use of large instruction windows coupled with aggressive out-oforder and prefetching capabilities has provided significant improvements in processor performance. In this paper...
We present a new bicriteria approximation algorithm for the degree-bounded minimum-cost spanning tree problem: Given an undirected graph with nonnegative edge weights and degree b...
Approximate queries on a collection of strings are important in many applications such as record linkage, spell checking, and Web search, where inconsistencies and errors exist in...