COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Improving Short-Text Classification using Unlabeled Data for Classification Problems
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
Active Sampling for Class Probability Estimation and Ranking
Machine Learning
Active feedback in ad hoc information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Large-scale text categorization by batch mode active learning
Proceedings of the 15th international conference on World Wide Web
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Constructing informative prior distributions from domain knowledge in text classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Incorporating diversity and density in active learning for relevance feedback
ECIR'07 Proceedings of the 29th European conference on IR research
Active relevance feedback for difficult queries
Proceedings of the 17th ACM conference on Information and knowledge management
Query Expansion Using External Evidence
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Adaptive relevance feedback in information retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Knowledge sciences in services automation: integration models and perspectives for service centers
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Language models for web object retrieval
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Hierarchical service analytics for improving productivity in an enterprise service center
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Collection-based sparse label propagation and its application on social group suggestion from photos
ACM Transactions on Intelligent Systems and Technology (TIST)
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
Relevance feedback exploiting query-specific document manifolds
Proceedings of the 20th ACM international conference on Information and knowledge management
Fully utilize feedbacks: language model based relevance feedback in information retrieval
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
A split-list approach for relevance feedback in information retrieval
Information Processing and Management: an International Journal
Incorporating statistical topic information in relevance feedback
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
High performance query expansion using adaptive co-training
Information Processing and Management: an International Journal
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Relevance feedback, which traditionally uses the terms in the relevant documents to enrich the user's initial query, is an effective method for improving retrieval performance. The traditional relevance feedback algorithms lead to overfitting because of the limited amount of training data and large term space. This paper introduces an online Bayesian logistic regression algorithm to incorporate relevance feedback information. The new approach addresses the overfitting problem by projecting the original feature space onto a more compact set which retains the necessary information. The new set of features consist of the original retrieval score, the distance to the relevant documents and the distance to non-relevant documents. To reduce the human evaluation effort in ascertaining relevance, we introduce a new active learning algorithm based on variance reduction to actively select documents for user evaluation. The new active learning algorithm aims to select feedback documents to reduce the model variance. The variance reduction approach leads to capturing relevance, diversity and uncertainty of the unlabeled documents in a principled manner. These are the critical factors of active learning indicated in previous literature. Experiments with several TREC datasets demonstrate the effectiveness of the proposed approach.