Name
Title | ||
|---|---|---|
Probabilistic generative ranking method based on multi-support vector domain description |
Abstract | ||
|---|---|---|
As the volume of database grows, retrieval and ordering of information according to relevance has become an important and challenging task. Ranking problem has recently been considered and formulated as a machine learning problem. Among the various learning-to-rank methods, the ranking support vector machines (SVMs) have been widely applied in various applications because of its state-of-the-art performance. In this paper, we propose a novel ranking method based on a probabilistic generative model approach. The proposed method utilizes multi-support vector domain description (multi-SVDD) and constructs pseudo-conditional probabilities for data pairs, thus enabling the construction of an efficient posterior probability function of relevance judgment of data pairs. Results of experiments on both synthetic and real large-scale datasets show that the proposed method can efficiently learn ranking functions better than ranking SVMs. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.ins.2013.05.001 | Inf. Sci. |
Keywords | Field | DocType |
probabilistic generative ranking method,ranking function,ranking support vector machine,novel ranking method,ranking problem,various learning-to-rank method,data pair,ranking svms,multi-support vector domain description,relevance judgment,classification,information retrieval,kernel method,learning to rank,support vector machines | Data mining,Learning to rank,Ranking SVM,Ranking,Support vector machine,Ranking (information retrieval),Artificial intelligence,Probabilistic logic,Relevance vector machine,Kernel method,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
247, | 0020-0255 | 6 |
PageRank | References | Authors |
0.45 | 27 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyu-Hwan Jung | 1 | 82 | 4.82 |
| Jaewook Lee | 2 | 72 | 8.87 |