RV-SVM: An Efficient Method for Learning Ranking SVM
نویسندگان
چکیده
Learning ranking (or preference) functions has become an important data mining task in recent years, as various applications have been found in information retrieval. Among rank learning methods, ranking SVM has been favorably applied to various applications, e.g., optimizing search engines, improving data retrieval quality. In this paper, we first develop a 1-norm ranking SVM that is faster in testing than the standard ranking SVM, and propose Ranking Vector SVM (RV-SVM) that revises the 1-norm ranking SVM for faster training. The number of variables in the RV-SVM is significantly smaller, thus the RV-SVM trains much faster than the other ranking SVMs. We experimentally compared the RV-SVM with the state-of-the-art rank learning method provided in SVM-light. The RV-SVM uses much less support vectors and trains much faster for nonlinear kernels than the SVM-light. The accuracies of RV-SVM and SVM-light are comparable on relatively large data sets. Our implementation of RV-SVM is posted at http://iis.postech.ac.kr/rv-svm.
منابع مشابه
SVM Tutorial - Classification, Regression and Ranking
Support Vector Machines(SVMs) have been extensively researched in the data mining and machine learning communities for the last decade and actively applied to applications in various domains. SVMs are typically used for learning classification, regression, or ranking functions, for which they are called classifying SVM, support vector regression (SVR), or ranking SVM (or RankSVM) respectively. ...
متن کاملLearning to Rank Questions for Community Question Answering with Ranking SVM
This paper presents our method to retrieve relevant queries given a new question in the context of Discovery Challenge: Learning to Re-Ranking Questions for Community Question Answering competition. In order to do that, a set of learning to rank methods was investigated to select an appropriate method. The selected method was optimized on training data by using a search strategy. After optimizi...
متن کاملCost-Sensitive Learning of SVM for Ranking
In this paper, we propose a new method for learning to rank. ‘Ranking SVM’ is a method for performing the task. It formulizes the problem as that of binary classification on instance pairs and performs the classification by means of Support Vector Machines (SVM). In Ranking SVM, the losses for incorrect classifications of instance pairs between different rank pairs are defined as the same. We n...
متن کاملMultiple hyperplanes Support Vector Machine for Ranking
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 r...
متن کاملVerification of unemployment benefits’ claims using Classifier Combination method
Unemployment insurance is one of the most popular insurance types in the modern world. The Social Security Organization is responsible for checking the unemployment benefits of individuals supported by unemployment insurance. Hand-crafted evaluation of unemployment claims requires a big deal of time and money. Data mining and machine learning as two efficient tools for data analysis can assist ...
متن کامل