Name
Abstract | ||
|---|---|---|
In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34481-7_27 | ICONIP |
Keywords | Field | DocType |
excellent performance,effective text classification,hybrid model,document community support vector,linear classifier,regularized linear classifier,naive bayes classifier,regularization,support vector machine | Pattern recognition,Naive Bayes classifier,Computer science,Support vector machine,Regularization (mathematics),Artificial intelligence,Margin classifier,Linear classifier,Bayes error rate,Machine learning,Bayes classifier,Quadratic classifier | Conference |
Volume | ISSN | Citations |
7664 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sharad Nandanwar | 1 | 11 | 2.16 |
| M. Narasimha Murty | 2 | 824 | 86.07 |