Name
Abstract | ||
|---|---|---|
Naive Bayes models have been very popular in several classification tasks. In this paper we study the application of these models to classification tasks where the data is sparse - i.e., a large number of possible outcomes do not appear in the data. Traditionally point estimates of the model parameters and in particular, point estimates based on the Laplace's rule have been popular for such sparse data. In this paper we investigate the use of the integrated likelihood using different techniques to determine the hyper-parameters of the prior distribution. The evaluations are conducted in the context of text classification. |
Year | Venue | Keywords |
|---|---|---|
2000 | PRICAI Workshop on Text and Web Mining | point estimation,naive bayes,sparse data,prior distribution |
Field | DocType | Citations |
Pattern recognition,Naive Bayes classifier,Computer science,Bayes factor,Bayesian programming,Bayesian hierarchical modeling,Artificial intelligence,Bayes error rate,Machine learning,Bayes classifier,Bayes' theorem | Conference | 4 |
PageRank | References | Authors |
0.52 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shivakumar Vaithyanathan | 1 | 2518 | 234.40 |
| J. Mao | 2 | 3234 | 358.27 |
| Byron Dom | 3 | 2600 | 825.93 |