SuperParent-One-Dependence Estimators (SPODEs) loosen Naive-Bayes’ attribute independence assumption by allowing each attribute to depend on a common single attribute (superpare...
Ying Yang, Kevin B. Korb, Kai Ming Ting, Geoffrey ...
A new scheme for the optimization of codebook sizes for HMMs and the generation of HMM ensembles is proposed in this paper. In a discrete HMM, the vector quantization procedure and...
Albert Hung-Ren Ko, Robert Sabourin, Alceu de Souz...
Clustering for the analysis of the gene expression profiles has been used for identifying the functions of the genes and of unknown genes. Since the genes usually belong to multipl...
A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear ma...
In this paper we address the problem of analyzing web log data collected at a typical online newspaper site. We propose a two-way clustering technique based on probability theory....
Hannes Wettig, Jussi Lahtinen, Tuomas Lepola, Petr...