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Speeding up Document Ranking with Rank-based Features
Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015Learning to Rank (LtR) is an effective machine learning me- thodology for inducing high-quality document ranking func- tions. Given a query and a candidate set of documents, where query-document pairs are represented by feature vec- tors, a machine-learned function is used to reorder this set.
Lucchese C +4 more
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Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007
Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking.
Xiubo Geng +3 more
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Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking.
Xiubo Geng +3 more
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2020
Low-rank representation (LRR), which constructs a robust low rank representation for data processing, has attracted much attention in the past decades. It is assumed that the data points lie on a low-dimensional subspace and the representation matrix of the data points is low-rank.
Haitao Zhao +3 more
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Low-rank representation (LRR), which constructs a robust low rank representation for data processing, has attracted much attention in the past decades. It is assumed that the data points lie on a low-dimensional subspace and the representation matrix of the data points is low-rank.
Haitao Zhao +3 more
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Feature ranking procedure for automatic feature extraction
2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 2016Classifier allows the user to classify between different classes based on the features acquired. The goals and applications of different classifiers are different. As the feature selection is one of the important criteria. In this paper we introduce a method of ranking the features of one class with respect to another and it tells the user that in the ...
Sarath Krishnan, S. Padmavathi
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Feature Ranking Computation Algorithm
International Journal of Organizational and Collective Intelligence, 2012This journal paper describes an algorithm of feature ranking computation, based both on a data set with a potentially excessive number of features and a neural network trained and tested on this set. Each member of the data set contains many features (inputs) and one output.
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2011
This chapter introduces a feature-based retrieval model based on Markov random fields (MRF model), which serves as the primary retrieval model throughout the remainder of the book. Although there are many different ways to formulate a general feature-based model for information retrieval, this work focuses on the MRF model because it satisfies the ...
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This chapter introduces a feature-based retrieval model based on Markov random fields (MRF model), which serves as the primary retrieval model throughout the remainder of the book. Although there are many different ways to formulate a general feature-based model for information retrieval, this work focuses on the MRF model because it satisfies the ...
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Greedy feature selection for ranking
Proceedings of the 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2011This paper is concerned with a study on the feature selection for ranking. Learning to rank is a useful tool for collaborative filtering and many other collaborative systems, which many algorithms have been proposed for dealing this issue. But feature selection methods receive little attention, despite of their importance in collaborative filtering ...
Hanjiang Lai +3 more
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