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 ...
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
—In this paper, we introduce a novel method to solve shape alignment problems. We use gray-scale “images” to represent source shapes, and propose a novel two-component Gaussi...
Background: There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large,...
This paper proposes a learning approach for the merging process in multilingual information retrieval (MLIR). To conduct the learning approach, we also present a large number of f...