Robots that can adapt and perform multiple tasks promise to be a powerful tool with many applications. In order to achieve such robots, control systems have to be constructed that...
Like many purely data-driven machine learning methods, Support Vector Machine (SVM) classifiers are learned exclusively from the evidence presented in the training dataset; thus ...
The Support Vector Machine (SVM) is an interesting classifier with excellent power of generalization. In this paper, we consider applying the SVM to semi-supervised learning. We p...
We present a comprehensive suite of experimentation on the subject of learning from imbalanced data. When classes are imbalanced, many learning algorithms can suffer from the pers...
Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napol...
We present a new method for regularized convex optimization and analyze it under both online and stochastic optimization settings. In addition to unifying previously known firstor...
John Duchi, Shai Shalev-Shwartz, Yoram Singer, Amb...