The nature of statistical learning theory
The nature of statistical learning theory
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
PREFER: a system for the efficient execution of multi-parametric ranked queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Constraint Classification: A New Approach to Multiclass Classification
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating Top-k Queries over Web-Accessible Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Active learning of label ranking functions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
RankFP: A Framework for Supporting Rank Formulation and Processing
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Discovering interesting patterns through user's interactive feedback
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Entropy-Driven online active learning for interactive calendar management
Proceedings of the 12th international conference on Intelligent user interfaces
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic optimized ranking for multimedia semantic concept detection via RVM
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Optimizing estimated loss reduction for active sampling in rank learning
Proceedings of the 25th international conference on Machine learning
Directly optimizing evaluation measures in learning to rank
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
RV-SVM: An Efficient Method for Learning Ranking SVM
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Probabilistic Ranking Support Vector Machine
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Efficient feature weighting methods for ranking
Proceedings of the 18th ACM conference on Information and knowledge management
Enabling multi-level relevance feedback on pubmed by integrating rank learning into DBMS
Proceedings of the third international workshop on Data and text mining in bioinformatics
Preference learning with extreme examples
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Active learning for ranking through expected loss optimization
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
RankSVR: can preference data help regression?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Fast active exploration for link-based preference learning using Gaussian processes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Selective sampling techniques for feedback-based data retrieval
Data Mining and Knowledge Discovery
Exact indexing for support vector machines
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Person-specific age estimation under ranking framework
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Nomogram visualization for ranking support vector machine
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Relevant knowledge helps in choosing right teacher: active query selection for ranking adaptation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Rule-based active sampling for learning to rank
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Active associative sampling for author name disambiguation
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
An efficient method for learning nonlinear ranking SVM functions
Information Sciences: an International Journal
Real-time top-n recommendation in social streams
Proceedings of the sixth ACM conference on Recommender systems
iSampling: framework for developing sampling methods considering user's interest
Proceedings of the 21st ACM international conference on Information and knowledge management
Variance maximization via noise injection for active sampling in learning to rank
Proceedings of the 21st ACM international conference on Information and knowledge management
Contextual and active learning-based affect-sensing from virtual drama improvisation
ACM Transactions on Speech and Language Processing (TSLP)
Active graph matching based on pairwise probabilities between nodes
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
TeRec: a temporal recommender system over tweet stream
Proceedings of the VLDB Endowment
iKernel: Exact indexing for support vector machines
Information Sciences: an International Journal
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Learning ranking (or preference) functions has been a major issue in the machine learning community and has produced many applications in information retrieval. SVMs (Support Vector Machines) - a classification and regression methodology - have also shown excellent performance in learning ranking functions. They effectively learn ranking functions of high generalization based on the "large-margin" principle and also systematically support nonlinear ranking by the "kernel trick". In this paper, we propose an SVM selective sampling technique for learning ranking functions. SVM selective sampling (or active learning with SVM) has been studied in the context of classification. Such techniques reduce the labeling effort in learning classification functions by selecting only the most informative samples to be labeled. However, they are not extendable to learning ranking functions, as the labeled data in ranking is relative ordering, or partial orders of data. Our proposed sampling technique effectively learns an accurate SVM ranking function with fewer partial orders. We apply our sampling technique to the data retrieval application, which enables fuzzy search on relational databases by interacting with users for learning their preferences. Experimental results show a significant reduction of the labeling effort in inducing accurate ranking functions.