We give a universal kernel that renders all the regular languages linearly separable. We are not able to compute this kernel efficiently and conjecture that it is intractable, but...
The paper introduces a new framework for feature learning in classification motivated by information theory. We first systematically study the information structure and present a n...
— Reinforcement Learning (RL) provides a promising new approach to systems performance management that differs radically from standard queuing-theoretic approaches making use of ...
Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, Mo...
We present a novel approach to statistical shape analysis of anatomical structures based on small sample size learning techniques. The high complexity of shape models used in medic...
Polina Golland, W. Eric L. Grimson, Martha Elizabe...
We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...