We present a novel loop transformation technique, particularly well suited for optimizing embedded compilers, where an increase in compilation time is acceptable in exchange for s...
A major difficulty for anomaly detection lies in discovering boundaries between normal and anomalous behavior, due to the deficiency of abnormal samples in the training phase. In...
While John Holland has always envisioned learning classifier systems (LCSs) as cognitive systems, most work on LCSs has focused on classification, datamining, and function appro...
In this paper, we show that a Bio-inspired classifier’s accuracy can be dramatically improved if it operates on intelligent features. We propose a novel set of intelligent feat...
M. Zubair Shafiq, Syed Ali Khayam, Muddassar Faroo...
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...