Current software interfaces fail to incorporate historical data from user interaction into their design. While some systems exhibit a minimalist use of history in the form of undo...
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...
This study presents a content-based image retrieval system IMALBUM based on local region of interest called object of interest (OOI). Each segmented or user-selected OOI is indexe...
This paper proposes a multiple dependent (or deferred) state sampling plan by variables for the inspection of normally distributed quality characteristics. The decision upon the a...
We address the e-rulemaking problem of reducing the manual labor required to analyze public comment sets. In current and previous work, for example, text categorization techniques...