Name
Abstract | ||
|---|---|---|
We apply manifold learning to a real data set of distressed and healthy companies for proper geometric tunning of similarity data points and visualization. While Isomap algorithm is often used in unsupervised learning our approach combines this algorithm with information of class labels for bankruptcy prediction. We compare prediction results with classifiers such as Support Vector Machines (SVM), Relevance Vector Machines (RVM) and the simple k-Nearest Neighbor (KNN) in the same data set and we show comparable accuracy of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-642-02490-0_88 | ICONIP (1) |
Keywords | Field | DocType |
bankruptcy analysis,relevance vector machines,support vector machines,isomap algorithm,comparable accuracy,prediction result,class label,bankruptcy prediction,similarity data point,relevance vector machine,unsupervised learning,support vector machine,manifold learning,k nearest neighbor | Online machine learning,Semi-supervised learning,Active learning (machine learning),Pattern recognition,Computer science,Support vector machine,Manifold alignment,Unsupervised learning,Bankruptcy prediction,Artificial intelligence,Relevance vector machine,Machine learning | Conference |
Volume | ISSN | ISBN |
5506 | 0302-9743 | 3-642-02489-0 |
Citations | PageRank | References |
12 | 0.68 | 8 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bernardete Ribeiro | 1 | 758 | 82.07 |
| Armando Vieira | 2 | 147 | 11.48 |
| João Duarte | 3 | 67 | 5.10 |
| Catarina Silva | 4 | 70 | 7.89 |
| João Carvalho das Neves | 5 | 41 | 6.48 |
| Qingzhong Liu | 6 | 588 | 44.77 |
| Andrew H. Sung | 7 | 1034 | 84.10 |