Learning kernel logistic regression in the presence of class label noise

DOI10.1016/j.patcog.2014.05.007zbMath1373.68314OpenAlexW2100683382MaRDI QIDQ1676947

Jakramate Bootkrajang, Ata Kabán

Publication date: 10 November 2017

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: http://pure-oai.bham.ac.uk/ws/files/16136049/Bootkrajang_Learning_kernal_logistic_Pattern_Recognition_2014.pdf

zbMATH Keywords

classificationmodel selectionlabel noisemultiple kernel learning

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Estimating mutual information for feature selection in the presence of label noise ⋮ Binary classification with corrupted labels ⋮ Learning from binary labels with instance-dependent noise ⋮ Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches ⋮ Infinite max-margin factor analysis via data augmentation ⋮ Quantum kernel logistic regression based Newton method ⋮ Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification

Uses Software

• LIBSVM

Cites Work

• Classification in the presence of class noise using a probabilistic kernel Fisher method
• Misclassification of the dependent variable in a discrete-response setting
• Robust supervised classification with mixture models: learning from data with uncertain labels
• Some results on Tchebycheffian spline functions and stochastic processes
• Linear Discriminant Analysis with Misallocation in Training Samples
• Partially Supervised Learning Using an EM‐Boosting Algorithm
• Robust Boosting Algorithm Against Mislabeling in Multiclass Problems
• Learning Kernel Perceptrons on Noisy Data Using Random Projections
• Discriminant Analysis When the Initial Samples Are Misclassified II: Non-Random Misclassification Models
• Soft margins for AdaBoost
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Learning kernel logistic regression in the presence of class label noise