We propose a method that detects the true direction of time series, by fitting an autoregressive moving average model to the data. Whenever the noise is independent of the previou...
In this paper, we introduce self-tuning histograms. Although similar in structure to traditional histograms, these histograms infer data distributions not by examining the data or...
Abstract. This paper is concerned with generalization issues for a decision tree learner for structured data called Alkemy. Motivated by error bounds established in statistical lea...
We develop an infrastructure for managing, indexing and serving multimedia content in digital libraries. This infrastructure follows the model of the web, and thereby is distribut...
Arjen P. de Vries, Brian S. Eberman, David E. Kova...
In this paper we present decomposable priors, a family of priors over structure and parameters of tree belief nets for which Bayesian learning with complete observations is tracta...