Name
Abstract | ||
|---|---|---|
If a simple and fast solution for one-class classification is required, the most; common approach is to assume a Gaussian distribution for the patterns of the single class. Bayesian classification then leads to a simple template matching. In this paper we show for two very different applications that the classification performance can be improved significantly if a more uniform subgaussian instead of a Gaussian class distribution is assumed. One application is face detection, the other is the detection of transcription factor binding sites on a genome. As for the Gaussian, the distance from a template, i.e., the distribution center, determines a pattern's class assignment. However, depending on the distribution assumed, maximum likelihood learning leads to different templates from the training data. These new templates lead to significant improvements of the classification performance. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-45243-0_45 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
template matching,gaussian distribution,maximum likelihood,bayesian classification,face detection,transcription factor binding site,one class classification | Template matching,One-class classification,Naive Bayes classifier,Pattern recognition,Support vector machine,Gaussian,Artificial intelligence,Gaussian process,Face detection,Pattern matching,Mathematics | Conference |
Volume | ISSN | Citations |
2781 | 0302-9743 | 2 |
PageRank | References | Authors |
0.41 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Madany Mamlouk | 1 | 37 | 9.52 |
| Jan T. Kim | 2 | 145 | 35.52 |
| Erhardt Barth | 3 | 653 | 58.33 |
| Michael Brauckmann | 4 | 107 | 8.91 |
| Thomas Martinetz | 5 | 1462 | 231.48 |