The goal of our current research is machine learning with the help and guidance of a knowledge base (KB). Rather than learning numerical models, our approach generates explicit sy...
We present and analyze a novel regularization technique based on enhancing our dataset with corrupted copies of our original data. The motivation is that since the learning algori...
A method is introduced to learn and represent similarity with linear operators in kernel induced Hilbert spaces. Transferring error bounds for vector valued large-margin classifie...
The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these mo...
This paper considers whether the seemingly disparate fields of Computational Intelligence (CI) and computer architecture can profit from each others’ principles, results and e...