We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
— The limited number of orthogonal channels and the autonomous installations of hotspots and home wireless networks often leave neighboring 802.11 basic service sets (BSS’s) op...
Chun-cheng Chen, Eunsoo Seo, Hwangnam Kim, Haiyun ...
Many facial image analysis methods rely on learningbased techniques such as Adaboost or SVMs to project classifiers based on the selection of local image filters (e.g., Haar and...
This paper presents a general learning framework for non-rigid registration of MR brain images. Given a set of training MR brain images, three major types of information are partic...
In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to impr...