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As a cost function, fisher linear discriminant criterion can be used to optimize the kernel function. However, the dataset may not be linearly separable even after kernel transformation in many applications. So, SVMs that use the kernel function optimized by fisher criterion can not ensure the performance. Motivated by this issue, an ensemble algorithm was proposed. Firstly, partitioning a dataset...
In this paper we propose an online multi-category support vector classifier dedicated to non-stationary environment. Our algorithm recursively discriminates between datasets of three or more classes, one sample at a time. With its incremental and decremental procedures, it can achieve an efficient update of the decision function after the incorporation/elimination of the incoming/oldest data. The...
A constructing method of fuzzy classifier using kernel k-means clustering algorithm is introduced in this paper. This constructing method are divided into three phases, namely clustering phase, fuzzy rule created phase and parameters modified phase. Firstly, the original sample space is mapped into a high dimensional feature space by selecting appropriate kernel function. In the feature space, training...
This paper presents a linear programming formulation for linear and nonlinear piecewise multi-classification support vector machines model for multi-category discrimination of sets or objects. The proposed model can be used to generate linear and nonlinear piecewise classifiers depending on the kernel function employed. Advantages of the linear programming multi-classification SVM formulation include...
Support Vector Machine (SVM) is a powerful classification technique based on the idea of structural risk minimization. Use of a kernel function enables the curse of dimensionality to be addressed. However, a proper kernel function for a certain problem is dependent on the specific dataset and as such there is no good method on how to choose a kernel function. In this paper, the choice of the kernel...
Maximizing the classification performance of the training data is a typical procedure in training a classifier. It is well known that training a Support Vector Machine (SVM) requires the solution of an enormous quadratic programming (QP) optimization problem. Serious challenges appeared in the training dilemma due to immense training and this could be solved using Sequential Minimal Optimization (SMO)...
We propose a feature selection criterion based on kernel discriminant analysis (KDA) for a n-class problem, which finds eigenvectors on which the projected class data are locally maximally separated. The proposed criterion is the sum of the objective function values of KDA associated with the n-1 eigenvectors. The criterion results in calculating the sum of n-1 eigenvalues associated with the eigenvectors...
Like most classification techniques, the existing support vector machines (SVM) approaches are challenged to correctly classify their input when the data points are either very close to the decision boundary or very dissimilar from the training data set. In both situations, most classifiers including SVMs will still give a prediction by assigning the test point to one of the classes. However, when...
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