Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Abstract. Grid computing is becoming a mainstream technology for multiinstitutional distributed resources sharing and system integration. Normally, the programmer's productivi...
We investigate the problem of pedestrian detection in
still images. Sliding window classifiers, notably using the
Histogram-of-Gradient (HOG) features proposed by Dalal
and Trig...
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
We introduce novel discriminative learning algorithms for dynamical systems. Models such as Conditional Random Fields or Maximum Entropy Markov Models outperform the generative Hi...