A general soft method for learning SVM classifiers with L1-norm penalty
Pattern Recognition
A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
Fuzzy relevance vector machine for learning from unbalanced data and noise
Pattern Recognition Letters
A comparative study on rough set based class imbalance learning
Knowledge-Based Systems
Margin calibration in SVM class-imbalanced learning
Neurocomputing
Kernel-matching pursuits with arbitrary loss functions
IEEE Transactions on Neural Networks
Context Switching Algorithm for Selective Multibiometric Fusion
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Fuzzy SVM for noisy data: a robust membership calculation method
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Online learning in biometrics: a case study in face classifier update
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Hybrid model based on SVM with Gaussian loss function and adaptive Gaussian PSO
Engineering Applications of Artificial Intelligence
Biometric classifier update using online learning: A case study in near infrared face verification
Image and Vision Computing
Cost-sensitive supported vector learning to rank imbalanced data set
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Employing multiple-kernel support vector machines for counterfeit banknote recognition
Applied Soft Computing
Fast support vector data descriptions for novelty detection
IEEE Transactions on Neural Networks
The forecasting model based on modified SVRM and PSO penalizing Gaussian noise
Expert Systems with Applications: An International Journal
Bayesian decision theory for support vector machines: Imbalance measurement and feature optimization
Expert Systems with Applications: An International Journal
Support vector machines using Bayesian-based approach in the issue of unbalanced classifications
Expert Systems with Applications: An International Journal
A novel algorithm applied to classify unbalanced data
Applied Soft Computing
Probabilistic outputs for twin support vector machines
Knowledge-Based Systems
SVM-based feature selection to optimize sensitivity-specificity balance applied to weaning
Computers in Biology and Medicine
Self-advising support vector machine
Knowledge-Based Systems
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This paper proposes a complete framework of posterior probability support vector machines (PPSVMs) for weighted training samples using modified concepts of risks, linear separability, margin, and optimal hyperplane. Within this framework, a new optimization problem for unbalanced classification problems is formulated and a new concept of support vectors established. Furthermore, a soft PPSVM with an interpretable parameter ν is obtained which is similar to the ν-SVM developed by Schölkopf et al., and an empirical method for determining the posterior probability is proposed as a new approach to determine ν. The main advantage of an PPSVM classifier lies in that fact that it is closer to the Bayes optimal without knowing the distributions. To validate the proposed method, two synthetic classification examples are used to illustrate the logical correctness of PPSVMs and their relationship to regular SVMs and Bayesian methods. Several other classification experiments are conducted to demonstrate that the performance of PPSVMs is better than regular SVMs in some cases. Compared with fuzzy support vector machines (FSVMs), the proposed PPSVM is a natural and an analytical extension of regular SVMs based on the statistical learning theory.