Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian ne...
By frame of reference transformations, an input variable in one coordinate system is transformed into an output variable in a different coordinate system depending on another inpu...
When constructing a Bayesian network, it can be advantageous to employ structural learning algorithms to combine knowledge captured in databases with prior information provided by...
By far, the support vector machines (SVM) achieve the state-of-theart performance for the text classification (TC) tasks. Due to the complexity of the TC problems, it becomes a ch...