A neural network approach to ordinal regression
| dc.creator | Cheng, Jianlin | |
| dc.date | 2007-04-08 | |
| dc.date.accessioned | 2026-07-07T07:55:42Z | |
| dc.date.available | 2026-07-07T07:55:42Z | |
| dc.description | Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe a simple and effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a generalization of the perceptron method for ordinal regression. On several benchmark datasets, our method (NNRank) outperforms a neural network classification method. Compared with the ordinal regression methods using Gaussian processes and support vector machines, NNRank achieves comparable performance. Moreover, NNRank has the advantages of traditional neural networks: learning in both online and batch modes, handling very large training datasets, and making rapid predictions. These features make NNRank a useful and complementary tool for large-scale data processing tasks such as information retrieval, web page ranking, collaborative filtering, and protein ranking in Bioinformatics. | |
| dc.description | 8 pages | |
| dc.identifier | https://arxiv.org/abs/0704.1028 | |
| dc.identifier | http://arxiv.org/abs/0704.1028 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/127059 | |
| dc.subject | Machine Learning | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.title | A neural network approach to ordinal regression | |
| dc.type | text |