In kernel methods, an interesting recent development seeks to learn a good kernel from empirical data automatically. In this paper, by regarding the transductive learning of the k...
We consider the problem of unsupervised learning from a matrix of data vectors where in each row the observed values are randomly permuted in an unknown fashion. Such problems ari...
Many techniques in the social sciences and graph theory deal with the problem of examining and analyzing patterns found in the underlying structure and associations of a group of ...
Jeremy Kubica, Andrew W. Moore, David Cohn, Jeff G...
The naive classifier is a well-established mathematical model whose simplicity, speed and accuracy have made it a popular choice for classification in AI and engineering. In this ...
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...