A probabilistic learning approach for document indexing
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Inferring probability of relevance using the method of logistic regression
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
A general language model for information retrieval
Proceedings of the eighth international conference on Information and knowledge management
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Fusion Via a Linear Combination of Scores
Information Retrieval
IEEE Intelligent Systems
Why Discretization Works for Naive Bayesian Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Tuning before feedback: combining ranking discovery and blind feedback for robust retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of Artificial Intelligence Research
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
A data driven approach to query expansion in question answering
IRQA '08 Coling 2008: Proceedings of the 2nd workshop on Information Retrieval for Question Answering
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We approached the problem as learning how to order documents by estimated relevance with respect to a user query. Our support vector machines based classifier learns from the relevance judgments available with the standard test collections and generalizes to new, previously unseen queries. For this, we have designed a representation scheme, which is based on the discrete representation of the local (lw) and global (gw) weighting functions, thus is capable of reproducing and enhancing the properties of such popular ranking functions as tf.idf, BM25 or those based on language models. Our tests with the standard test collections have demonstrated the capability of our approach to achieve the performance of the best known scoring functions solely from the labeled examples and without taking advantage of knowing those functions or their important properties or parameters.