Multiple hyperplanes Support Vector Machine for Ranking
نویسنده
چکیده
Learning to rank have become a famous problem for document retrieval and other applications. Recently, several machine learning techniques are applied into this task. Ranking SVM, which uses Support Vector Machine (SVM) to perform the problem, is an example. In this paper, we present a novel approach which also based on SVM. We consider the modification of SVM by adding bias term to different ranking task, and constructing a set of parallel hyperplanes to perform ranking. We evaluate the proposed method on the dataset LETOR which including the web search datasets TREC and OHSUMED.
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