The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
We present a novel framework for multi-label learning that explicitly addresses the challenge arising from the large number of classes and a small size of training data. The key a...
Currently, the genome sequencing community is producing shotgun sequence data at a very high rate, but genome finishing is not keeping pace, even with the help from several automa...
We investigate the following lower bound methods for regular languages: The fooling set technique, the extended fooling set technique, and the biclique edge cover technique. It is ...
In a real-time system, it is attractive to use cluster computing system for realizing high performance and high availability. The objectives of the real-time clusters are maximize...