The problem of time series classification has attracted great interest in the last decade. However current research assumes the existence of large amounts of labeled training data...
We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we cal...
David Heckerman, Dan Geiger, David Maxwell Chicker...
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
Data mining has emerged to address the problem of transforming data into useful knowledge. Although most data mining techniques, such as Association Rules, substantially reduce th...
Data mining promises to discover valid and potentially useful patterns in data. Often, discovered patterns are not useful to the user. "Actionability" addresses this pro...