When the number of labeled examples is limited, traditional supervised feature selection techniques often fail due to sample selection bias or unrepresentative sample problem. To ...
This paper discusses the problem of knowledge discovery in image databases with particular focus on the issues which arise when absolute ground truth is not available. It is often...
Padhraic Smyth, Michael C. Burl, Usama M. Fayyad, ...
The Random forest classifier comes to be the working horse for visual recognition community. It predicts the class label of an input data by aggregating the votes of multiple tree...
Outlier detection can uncover malicious behavior in fields like intrusion detection and fraud analysis. Although there has been a significant amount of work in outlier detection, ...
We propose mixtures of hidden Markov models for modelling clickstreams of web surfers. Hence, the page categorization is learned from the data without the need for a (possibly cumb...