We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labele...
In this paper we present a novel strategy, DragPushing, for improving the performance of text classifiers. The strategy is generic and takes advantage of training errors to succes...
Songbo Tan, Xueqi Cheng, Moustafa Ghanem, Bin Wang...
In this paper we present StarNT, a dictionary-based fast lossless text transform algorithm. With a static generic dictionary, StarNT achieves a superior compression ratio than alm...
Web caching and content replication techniques emerged to solve performance problems related to the Web. We propose a generic non-parametric heuristic method that integrates both ...
Konstantinos Stamos, George Pallis, Charilaos Thom...
In this paper, we present performance results from mapping five real-world DSP applications on an embedded system-on-chip that incorporates coarse-grain reconfigurable logic with ...
Michalis D. Galanis, Grigoris Dimitroulakos, Const...