An intuitive approach to utilizing unlabeled data in kernel-based classification algorithms is to simply treat unknown labels as additional optimization variables. For marginbased...
Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chape...
In Multi-Agent learning, agents must learn to select actions that maximize their utility given the action choices of the other agents. Cooperative Coevolution offers a way to evol...
We cast some new insights into solving the digital matting
problem by treating it as a semi-supervised learning
task in machine learning. A local learning based approach
and a g...
Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the train...
Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of supervised learning and h...