We propose a new machine learning paradigm called Graph Transformer Networks that extends the applicability of gradient-based learning algorithms to systems composed of modules th...
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly access...
Machine learning techniques are applicable to computer system optimization. We show that shared memory multiprocessors can successfully utilize machine learning algorithms for mem...
M. F. Sakr, Steven P. Levitan, Donald M. Chiarulli...
In this paper, a novel diagnosis method is proposed. The proposed technique uses machine learning techniques instead of traditional cause-effect and/or effect-cause analysis. The ...
Support vector machines (SVMs) have been widely used in multimedia retrieval to learn a concept in order to find the best matches. In such a SVM active learning environment, the ...