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On rank selection probabilities
IEEE Transactions on Signal Processing, 1994The concept of rank selection probabilities for stack filters has previously been introduced. If the probabilities are known, then the output distribution for i.i.d. input follows easily. There is another expression for the output distribution of a stack filter using certain quantities called A/sub i/.
Pauli Kuosmanen +2 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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Ranking and Selecting Services
2009Service composition is the most recent approach to software reuse. The interactions among services propose many problems already approached in the composition of software components even if introducing more issues related to the run-time composition that is very limited in the components world.
Sillitti A, Succi G
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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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Ranking and Contextual Selection
Operations ResearchContext-Sensitive Simulation-Based Decisions When There Is No Time to Simulate Stochastic simulation is a powerful tool for discovering system design decisions that are the best possible (optimal) when averaged over real-world uncertainty. However, in applications such as personalized medicine and web content optimization, even better decisions can ...
Gregory Keslin +4 more
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Rank-order tournaments and selection
Journal of Economics, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Clark, Derek J., Riis, Christian
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Aggregating Ranked Services for Selection
2014 IEEE International Conference on Services Computing, 2014In this paper we propose a method for aggregating ranked services. The ranked services are generated from multiple user requests for the same service domain. First, a service search for each individual request is performed and the search results are ranked based on the user's personalized non-functional attributes and trade-offs. Next, the ranked lists
Kenneth K. Fletcher +2 more
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Statistical Rank Selection for Incomplete Low-rank Matrices
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019We consider the problem of determining the rank in the low-rank matrix completion. We propose a statistical model for noisy observation. It is important for many existing algorithms and sometimes has practical meanings. We construct a test statistics for the low rank approximation problem.
Rui Zhang 0053 +2 more
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Rank conditioned rank selection filters for signal restoration
IEEE Transactions on Image Processing, 1994A class of nonlinear filters called rank conditioned rank selection (RCRS) filters is developed and analyzed in this paper. The RCRS filters are developed within the general framework of rank selection (RS) filters, which are filters constrained to output an order statistic from the observation set. Many previously proposed rank order based filters can
Hardie, Russell C., Barner, Kenneth E.
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Fast Feature Selection for Learning to Rank
Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, 2016An emerging research area named Learning-to-Rank (LtR) has shown that effective solutions to the ranking problem can leverage machine learning techniques applied to a large set of features capturing the relevance of a candidate document for the user query. Large-scale search systems must however answer user queries very fast, and the computation of the
Gigli A +3 more
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