Active cleaning of label noise
暂无分享,去创建一个
Lawrence O. Hall | Dmitry B. Goldgof | Matthew Shreve | Rangachar Kasturi | Sergiy Fefilatyev | Kurt Kramer | Ekambaram Rajmadhan | L. Hall | R. Kasturi | D. Goldgof | Sergiy Fefilatyev | Ekambaram Rajmadhan | K. Kramer | M. Shreve | Matthew Shreve | Dmitry Goldgof
[1] Stefanie Nowak,et al. Using one-class SVM outliers detection for verification of collaboratively tagged image training sets , 2009, 2009 IEEE International Conference on Multimedia and Expo.
[2] Chih-Jen Lin,et al. Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..
[3] Sam Kwong,et al. A noise-detection based AdaBoost algorithm for mislabeled data , 2012, Pattern Recognit..
[4] Xindong Wu,et al. Eliminating Class Noise in Large Datasets , 2003, ICML.
[5] Pang-Ning Tan,et al. Kernel Based Detection of Mislabeled Training Examples , 2007, SDM.
[6] Liva Ralaivola,et al. Learning SVMs from Sloppily Labeled Data , 2009, ICANN.
[7] Fabrice Muhlenbach,et al. Identifying and Handling Mislabelled Instances , 2004, Journal of Intelligent Information Systems.
[8] J. Weston,et al. Support Vector Machine Solvers , 2007 .
[9] H WittenIan,et al. The WEKA data mining software , 2009 .
[10] Gunnar Rätsch,et al. Soft Margins for AdaBoost , 2001, Machine Learning.
[11] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[12] Blaine Nelson,et al. Support Vector Machines Under Adversarial Label Noise , 2011, ACML.
[13] G DietterichThomas. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees , 2000 .
[14] Carole Lartizien,et al. Handling uncertainties in SVM classification , 2011, 2011 IEEE Statistical Signal Processing Workshop (SSP).
[15] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[16] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[17] V. Vapnik. Estimation of Dependences Based on Empirical Data , 2006 .
[18] Kanishka Bhaduri,et al. Distributed anomaly detection using 1‐class SVM for vertically partitioned data , 2011, Stat. Anal. Data Min..
[19] Bernhard Schölkopf,et al. Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.
[20] Carla E. Brodley,et al. Identifying Mislabeled Training Data , 1999, J. Artif. Intell. Res..
[21] Carla E. Brodley,et al. Challenges and Opportunities in Applied Machine Learning , 2012, AI Mag..
[22] Carla E. Brodley,et al. Strategic targeting of outliers for expert review , 2010 .
[23] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[24] Lawrence O. Hall,et al. Label-noise reduction with support vector machines , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).
[25] Saso Dzeroski,et al. Noise detection and elimination in data preprocessing: Experiments in medical domains , 2000, Appl. Artif. Intell..
[26] Khoa N. Le. A mathematical approach to edge detection in hyperbolic-distributed and Gaussian-distributed pixel-intensity images using hyperbolic and Gaussian masks , 2011, Digit. Signal Process..
[27] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[28] Fariborz Mahmoudi,et al. Robust Handwritten Character Recognition with Features Inspired by Visual Ventral Stream , 2008, Neural Processing Letters.
[29] Isabelle Guyon,et al. Discovering Informative Patterns and Data Cleaning , 1996, Advances in Knowledge Discovery and Data Mining.
[30] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[31] Rebecca Castano,et al. Improving onboard analysis of Hyperion images by filtering mislabeled training data examples , 2009, 2009 IEEE Aerospace conference.
[32] Ana I. González Acuña. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, Boosting, and Randomization , 2012 .
[33] Thomas G. Dietterich. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.
[34] Carla E. Brodley,et al. Class Noise Mitigation Through Instance Weighting , 2007, ECML.
[35] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[36] Carla E. Brodley,et al. Generating High-Quality Training Data for Automated Land-Cover Mapping , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.
[37] Paulo Cortez,et al. Modeling wine preferences by data mining from physicochemical properties , 2009, Decis. Support Syst..
[38] Janaina Mourão Miranda,et al. Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine , 2011, NeuroImage.