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Query-level stability and generalization in learning to rank (2008)
| Content Provider | CiteSeerX |
|---|---|
| Author | Lan, Yanyan Ma, Zhiming Li, Hang |
| Description | This paper is concerned with the generaliza-tion ability of learning to rank algorithms for information retrieval (IR). We point out that the key for addressing the learning problem is to look at it from the viewpoint of query. We define a number of new concepts, including query-level loss, query-level risk, and query-level stability. We then analyze the general-ization ability of learning to rank algorithms by giving query-level generalization bounds to them using query-level stability as a tool. Such an analysis is very helpful for us to de-rive more advanced algorithms for IR. We ap-ply the proposed theory to the existing algo-rithms of Ranking SVM and IRSVM. Exper-imental results on the two algorithms verify the correctness of the theoretical analysis. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2008-01-01 |
| Publisher Institution | In Proceedings of the 25th International Conference on Machine Learning (ICML 2008 |
| Access Restriction | Open |
| Subject Keyword | Query-level Loss Advanced Algorithm Exper-imental Result Query-level Generalization Bound New Concept Theoretical Analysis Query-level Stability Query-level Risk General-ization Ability Learning Problem Ranking Svm Generaliza-tion Ability Information Retrieval |
| Content Type | Text |
| Resource Type | Article |
