An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Classification of Faces in Man and Machine
Neural Computation
An experimental comparison of gender classification methods
Pattern Recognition Letters
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
The Journal of Machine Learning Research
Gender Classification by Combining Facial and Hair Information
Advances in Neuro-Information Processing
Gender Classification Based on Support Vector Machine with Automatic Confidence
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Classifier combination based on confidence transformation
Pattern Recognition
Multi-view gender classification using local binary patterns and support vector machines
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
The generalization error of the symmetric and scaled support vector machines
IEEE Transactions on Neural Networks
LDA/SVM driven nearest neighbor classification
IEEE Transactions on Neural Networks
Recognizing human gender in computer vision: a survey
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Three robust features extraction approaches for facial gender classification
The Visual Computer: International Journal of Computer Graphics
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In this paper, we propose a support vector machine with automatic confidence (SVMAC) for pattern classification. The main contributions of this work to learning machines are twofold. One is that we develop an algorithm for calculating the label confidence value of each training sample. Thus, the label confidence values of all of the training samples can be considered in training support vector machines. The other one is that we propose a method for incorporating the label confidence value of each training sample into learning and derive the corresponding quadratic programming problems. To demonstrate the effectiveness of the proposed SVMACs, a series of experiments are performed on three benchmarking pattern classification problems and a challenging gender classification problem. Experimental results show that the generalization performance of our SVMACs is superior to that of traditional SVMs.