We propose a novel approach to experience collection for autonomous service robots performing complex activities. This approach enables robots to data for many learning problems at...
In this paper we present a methodology to estimate rates of enzymatic reactions in metabolic pathways. Our methodology is based on applying stochastic logic learning in ensemble le...
Several published reports show that instancebased learning algorithms yield high classification accuracies and have low storage requirements during supervised learning application...
Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. Intuitively, discovering a good feature rep...
Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, Qi...
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...