OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The stochastic approach for link-structure analysis (SALSA) and the TKC effect
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Domain described support vector classifier for multi-classification problems
Pattern Recognition
FRank: a ranking method with fidelity loss
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Information Sciences: an International Journal
Constructing sparse kernel machines using attractors
IEEE Transactions on Neural Networks
Improving memory-based collaborative filtering via similarity updating and prediction modulation
Information Sciences: an International Journal
Fast support-based clustering method for large-scale problems
Pattern Recognition
Dynamic Dissimilarity Measure for Support-Based Clustering
IEEE Transactions on Knowledge and Data Engineering
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Dynamic pattern denoising method using multi-basin system with kernels
Pattern Recognition
An efficient method for learning nonlinear ranking SVM functions
Information Sciences: an International Journal
Equilibrium-Based Support Vector Machine for Semisupervised Classification
IEEE Transactions on Neural Networks
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
Sentiment topic models for social emotion mining
Information Sciences: an International Journal
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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.