Learning kernel logistic regression in the presence of class label noise

[1]  Ata Kabán,et al.  Classification of mislabelled microarrays using robust sparse logistic regression , 2013, Bioinform..

[2]  Ata Kabán,et al.  Label-Noise Robust Logistic Regression and Its Applications , 2012, ECML/PKDD.

[3]  Rong Jin,et al.  Multiple Kernel Learning from Noisy Labels by Stochastic Programming , 2012, ICML.

[4]  Blaine Nelson,et al.  Support Vector Machines Under Adversarial Label Noise , 2011, ACML.

[5]  Benoît Frénay,et al.  Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs , 2011, ECML/PKDD.

[6]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[7]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[8]  Ata Kabán,et al.  Multi-class classification in the presence of labelling errors , 2011, ESANN.

[9]  Zenglin Xu,et al.  Simple and Efficient Multiple Kernel Learning by Group Lasso , 2010, ICML.

[10]  Gerardo Hermosillo,et al.  Learning From Crowds , 2010, J. Mach. Learn. Res..

[11]  Gavin C. Cawley,et al.  On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation , 2010, J. Mach. Learn. Res..

[12]  Charles Bouveyron,et al.  Robust supervised classification with mixture models: Learning from data with uncertain labels , 2009, Pattern Recognit..

[13]  Liva Ralaivola,et al.  Learning SVMs from Sloppily Labeled Data , 2009, ICANN.

[14]  Shie Mannor,et al.  Robustness and Regularization of Support Vector Machines , 2008, J. Mach. Learn. Res..

[15]  Theodoros Damoulas,et al.  Pattern recognition with a Bayesian kernel combination machine , 2009, Pattern Recognit. Lett..

[16]  Brendan T. O'Connor,et al.  Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.

[17]  Takafumi Kanamori,et al.  Robust Boosting Algorithm Against Mislabeling in Multiclass Problems , 2008, Neural Computation.

[18]  Marcel J. T. Reinders,et al.  Classification in the presence of class noise using a probabilistic Kernel Fisher method , 2007, Pattern Recognit..

[19]  Liva Ralaivola,et al.  Learning Kernel Perceptrons on Noisy Data Using Random Projections , 2007, ALT.

[20]  Gavin C. Cawley,et al.  Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters , 2007, J. Mach. Learn. Res..

[21]  Enrico Blanzieri,et al.  Detecting potential labeling errors in microarrays by data perturbation , 2006, Bioinform..

[22]  Koby Crammer,et al.  Robust Support Vector Machine Training via Convex Outlier Ablation , 2006, AAAI.

[23]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[24]  Nello Cristianini,et al.  A statistical framework for genomic data fusion , 2004, Bioinform..

[25]  Zhi-Hua Zhou,et al.  Editing Training Data for kNN Classifiers with Neural Network Ensemble , 2004, ISNN.

[26]  Li Hsu,et al.  Partially Supervised Learning Using an EM‐Boosting Algorithm , 2004, Biometrics.

[27]  Gunnar Rätsch,et al.  Soft Margins for AdaBoost , 2001, Machine Learning.

[28]  Fabrice Muhlenbach,et al.  Identifying and Handling Mislabelled Instances , 2004, Journal of Intelligent Information Systems.

[29]  Roberto Alejo,et al.  Analysis of new techniques to obtain quality training sets , 2003, Pattern Recognit. Lett..

[30]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Bernhard Schölkopf,et al.  Estimating a Kernel Fisher Discriminant in the Presence of Label Noise , 2001, ICML.

[32]  Jason Weston,et al.  Gene functional classification from heterogeneous data , 2001, RECOMB.

[33]  Eduardo Gasca,et al.  Decontamination of Training Samples for Supervised Pattern Recognition Methods , 2000, SSPR/SPR.

[34]  Andrian Marcus,et al.  Data Cleansing: Beyond Integrity Analysis 1 , 2000 .

[35]  Andrian Marcus,et al.  Data Cleansing: Beyond Integrity Analysis , 2000, IQ.

[36]  Gilles Celeux,et al.  A Component-Wise EM Algorithm for Mixtures , 2001, 1201.5913.

[37]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[38]  Carla E. Brodley,et al.  Identifying Mislabeled Training Data , 1999, J. Artif. Intell. Res..

[39]  J. Hausman,et al.  Misclassification of the dependent variable in a discrete-response setting , 1998 .

[40]  L. Magder,et al.  Logistic regression when the outcome is measured with uncertainty. , 1997, American journal of epidemiology.

[41]  Haym Hirsh,et al.  Classifier Learning from Noisy Data as Probabilistic Evidence Combination , 1992, AAAI.

[42]  Gábor Lugosi,et al.  Learning with an unreliable teacher , 1992, Pattern Recognit..

[43]  Subhas C. Nandy,et al.  Efficiency of discriminant analysis when initial samples are classified stochastically , 1990, Pattern Recognit..

[44]  R. Chhikara,et al.  Linear discriminant analysis with misallocation in training samples , 1984 .

[45]  P. Lachenbruch Discriminant Analysis When the Initial Samples are Misclassified II: Non-Random Misclassification Models , 1974 .

[46]  G. Wahba,et al.  Some results on Tchebycheffian spline functions , 1971 .

[47]  P. Lachenbruch Discriminant Analysis When the Initial Samples Are Misclassified , 1966 .