In transfer learning we aim to solve new problems using fewer examples using information gained from solving related problems. Transfer learning has been successful in practice, a...
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
We address the problem of automatically acquiring case frame patterns (selectional patterns) from large corpus data. In particular, we l)ropose a method of learning dependencies b...
This paper introduces a new concept, a decision tree (or list) over tree patterns, which is a natural extension of a decision tree (or decision list), for dealing with tree struct...
This paper considers the regularized learning algorithm associated with the leastsquare loss and reproducing kernel Hilbert spaces. The target is the error analysis for the regres...