The nature of statistical learning theory
The nature of statistical learning theory
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning large margin classifiers locally and globally
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Hidden space support vector machines
IEEE Transactions on Neural Networks
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Considering some limitations of the existing large margin classifier (LMC) and support vector machines (SVMs), this paper develops a modified linear projection classification algorithm based on the margin, termed modified large margin classifier in hidden space (MLMC). MLMC can seek a better classification hyperplane than LMC and SVMs through integrating the within-class variance into the objective function of LMC. Also, the kernel functions in MLMC are not required to satisfy the Mercer's condition. Compared with SVMs, MLMC can use more kinds of kernel functions. Experiments on the FERET face database confirm the feasibility and effectiveness of the proposed method.