Conference paper
Modeling text with generalizable Gaussian mixtures
We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss the relation between supervised and unsupervised learning in the test data.
Finally, we implement a novelty detector based on the density model.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2000 |
| Pages: | 3494-3497 |
| Proceedings: | 1995 IEEE International Conference on Acoustics, Speech, and Signal Processing |
| ISBN: | 0780362934 and 9780780362932 |
| ISSN: | 2379190x and 15206149 |
| Types: | Conference paper |
| DOI: | 10.1109/ICASSP.2000.860154 |
| ORCIDs: | Hansen, Lars Kai , Nielsen, Finn Årup and Larsen, Jan |
Cost function Detectors Feature extraction Gaussian processes Histograms Information retrieval Large scale integration Mathematical model Pattern recognition Statistics Supervised learning Web browser Web visualization computational complexity density model based detector generalized Gaussian mixtures information retrieval model complexity pattern recognition representation dimension sample size supervised learning test data test modeling text mining text representation unsupervised learning
