Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
About the use of fuzzy clustering techniques for fuzzy model identification
Fuzzy Sets and Systems
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Support Vector Data Description
Machine Learning
Domain described support vector classifier for multi-classification problems
Pattern Recognition
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiplicative Updates for Nonnegative Quadratic Programming
Neural Computation
Data description and noise filtering based detection with its application and performance comparison
Expert Systems with Applications: An International Journal
Fuzzy multi-class classifier based on support vector data description and improved PCM
Expert Systems with Applications: An International Journal
Multiclass classification based on extended support vector data description
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new multi-class support vector machine with multi-sphere in the feature space
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Wavelet support vector machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Additive Support Vector Machines for Pattern Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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New decision-making function for multi-class support vector domain description (SVDD) classifier using the conception of attraction force was proposed in this paper. As for multi-class classification problems, multiple optimized hyperspheres which described each class of dataset were constructed separately similar with in the preliminary SVDD. Then new decision-making function was proposed based on the parameters of the multi-class SVDD model with the conception of attraction force. Experimental results showed that the proposed decision-making function for multi-class SVDD classifier is more accurate than the decision-making function using local density degree.