Many interesting problems, such as power grids, network switches, and tra c ow, that are candidates for solving with reinforcement learningRL, alsohave properties that make distri...
Jeff G. Schneider, Weng-Keen Wong, Andrew W. Moore...
Data mining techniques have become central to many applications. Most of those applications rely on so called supervised learning algorithms, which learn from given examples in th...
This paper presents a method of extracting subprograms from background knowledge. Most studies on learning logic programs so far developed are mainly concerned with pure Prolog, so...
To learn a metric for query?based operations, we combine the concept underlying manifold learning algorithms and the minimum volume ellipsoid metric in a unified algorithm to find...
The assumptions behind linear classifiers for categorical data are examined and reformulated in the context of the multinomial manifold, the simplex of multinomial models furnishe...