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» Online selection of diverse results
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97
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KDD
2012
ACM
187views Data Mining» more  KDD 2012»
13 years 2 days ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
62
Voted
PPSN
2004
Springer
15 years 3 months ago
Ensemble Learning with Evolutionary Computation: Application to Feature Ranking
Abstract. Exploiting the diversity of hypotheses produced by evolutionary learning, a new ensemble approach for Feature Selection is presented, aggregating the feature rankings ext...
Kees Jong, Elena Marchiori, Michèle Sebag
SAC
2009
ACM
15 years 4 months ago
Music retrieval based on a multi-samples selection strategy for support vector machine active learning
In active learning based music retrieval systems, providing multiple samples to the user for feedback is very necessary. In this paper, we present a new multi-samples selection st...
Tian-Jiang Wang, Gang Chen, Perfecto Herrera
ICC
2007
IEEE
128views Communications» more  ICC 2007»
15 years 4 months ago
An Efficient Selective Receiver for STBC Scheme
—In this paper, we propose an efficient selective receiver for space-time block coding (STBC) scheme. In this proposed scheme, we divide the received signals into two groups. By ...
Lijun Liu, Sooyoung Kim, Myoung-Seob Lim
91
Voted
ICDAR
2003
IEEE
15 years 2 months ago
Automatic Feature Selection with Applications to Script Identification of Degraded Documents
Current approaches to script identification rely on hand-selected features and often require processing a significant part of the document to achieve reliable identification. We p...
Vitaly Ablavsky, Mark R. Stevens