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An investigation into the application of ensemble learning for entailment classificationROONEY, Niall; HUI WANG; TAYLOR, Philip S et al.Information processing & management. 2014, Vol 50, Num 1, pp 87-103, issn 0306-4573, 17 p.Article

Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithmsOZCIFT, Akin; GULTEN, Arif.Computer methods and programs in biomedicine (Print). 2011, Vol 104, Num 3, pp 443-451, issn 0169-2607, 9 p.Article

A novel hybrid ensemble learning paradigm for nuclear energy consumption forecastingLING TANG; YU, Lean; SHUAI WANG et al.Applied energy. 2012, Vol 93, pp 432-443, issn 0306-2619, 12 p.Article

RAMOBoost: Ranked Minority Oversampling in BoostingSHENG CHEN; HAIBO HE; GARCIA, Edwardo A et al.IEEE transactions on neural networks. 2010, Vol 21, Num 10, pp 1624-1642, issn 1045-9227, 19 p.Article

Non-strict heterogeneous StackingROONEY, Niall; PATTERSON, David; NUGENT, Chris et al.Pattern recognition letters. 2007, Vol 28, Num 9, pp 1050-1061, issn 0167-8655, 12 p.Article

Automatic identification of music performers with learning ensemblesSTAMATATOS, Efstathios; WIDMER, Gerhard.Artificial intelligence. 2005, Vol 165, Num 1, pp 37-56, issn 0004-3702, 20 p.Article

A novel classification method based on the ensemble learning and feature selection for aluminophosphate structural predictionMINGHAI YAO; MIAO QI; JINSONG LI et al.Microporous and mesoporous materials. 2014, Vol 186, pp 201-206, issn 1387-1811, 6 p.Article

Bagging Constraint Score for feature selection with pairwise constraintsDAN SUN; DAOQIANG ZHANG.Pattern recognition. 2010, Vol 43, Num 6, pp 2106-2118, issn 0031-3203, 13 p.Article

Ensemble learning of linear perceptrons : On-line learning theoryHARA, Kazuyuki; OKADA, Masato.Journal of the Physical Society of Japan. 2005, Vol 74, Num 11, pp 2966-2972, issn 0031-9015, 7 p.Article

Online Learning of Perceptron from Noisy Data: A Case in which Both Student and Teacher Suffer from External NoiseUEZU, Tatsuya; YAMAGUCHI, Sachi; YOSHIDA, Mika et al.Journal of the Physical Society of Japan. 2010, Vol 79, Num 9, issn 0031-9015, 094003.1-094003.13Article

Exploration of classification confidence in ensemble learningLEIJUN LI; QINGHUA HU; XIANGQIAN WU et al.Pattern recognition. 2014, Vol 47, Num 9, pp 3120-3131, issn 0031-3203, 12 p.Article

Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigmsIBRAHIM ERDAL, Halil; KARAKURT, Onur.Journal of hydrology (Amsterdam). 2013, Vol 477, pp 119-128, issn 0022-1694, 10 p.Article

Design of a visual perception model with edge-adaptive Gabor filter and support vector machine for traffic sign detectionPARK, Jung-Guk; KIM, Kyung-Joong.Expert systems with applications. 2013, Vol 40, Num 9, pp 3679-3687, issn 0957-4174, 9 p.Article

Pairwise meta-rules for better meta-learning-based algorithm rankingQUAN SUN; PFAHRINGER, Bernhard.Machine learning. 2013, Vol 93, Num 1, pp 141-161, issn 0885-6125, 21 p.Conference Paper

Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methodsANTAL, Bálint; HAJDU, András.Pattern recognition. 2012, Vol 45, Num 1, pp 264-270, issn 0031-3203, 7 p.Article

Robust ensemble learning for mining noisy data streamsPENG ZHANG; XINGQUAN ZHU; YONG SHI et al.Decision support systems. 2011, Vol 50, Num 2, pp 469-479, issn 0167-9236, 11 p.Article

Uncertainty propagation in vegetation distribution models based on ensemble classifiersPETERS, Jan; VERHOEST, Niko E. C; SAMSON, Roeland et al.Ecological modelling. 2009, Vol 220, Num 6, pp 791-804, issn 0304-3800, 14 p.Article

Solving multi-instance problems with classifier ensemble based on constructive clustering : Mining low-quality dataZHOU, Zhi-Hua; ZHANG, Min-Ling.Knowledge and information systems. 2007, Vol 11, Num 2, pp 155-170, 16 p.Article

Ensemble methods for multi-label classificationROKACH, Lior; SCHCLAR, Alon; ITACH, Ehud et al.Expert systems with applications. 2014, Vol 41, Num 16, pp 7507-7523, issn 0957-4174, 17 p.Article

Boosting regression methods based on a geometric conversion approach: Using SVMs base learnersFENG GAO; PENG KOU; LIN GAO et al.Neurocomputing (Amsterdam). 2013, Vol 113, pp 67-87, issn 0925-2312, 21 p.Article

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classificationWEBB, Geoffrey I; BOUGHTON, Janice R; FEI ZHENG et al.Machine learning. 2012, Vol 86, Num 2, pp 233-272, issn 0885-6125, 40 p.Article

Predicting Pedestrian Counts in Crowded Scenes With Rich and High-Dimensional FeaturesJUNPING ZHANG; BEN TAN; FEI SHA et al.IEEE Transactions on intelligent transportation systems. 2011, Vol 12, Num 4, pp 1037-1046, issn 1524-9050, 10 p.Article

Statistical Instance-Based Pruning in Ensembles of Independent ClassifiersHERNANDEZ-LOBATO, Daniel; MARTINEZ-MUNOZ, Gonzalo; SUAREZ, Alberto et al.IEEE transactions on pattern analysis and machine intelligence. 2009, Vol 31, Num 2, pp 364-369, issn 0162-8828, 6 p.Article

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigmYU, Lean; SHOUYANG WANG; KIN KEUNG LAI et al.Energy economics. 2008, Vol 30, Num 5, pp 2623-2635, issn 0140-9883, 13 p.Article

An improved boosting based on feature selection for corporate bankruptcy predictionGANG WANG; JIAN MA; SHANLIN YANG et al.Expert systems with applications. 2014, Vol 41, Num 5, pp 2353-2361, issn 0957-4174, 9 p.Article

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