Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
A classification method based on generalized eigenvalue problems
Optimization Methods & Software - Systems Analysis, Optimization and Data Mining in Biomedicine
Nonparallel plane proximal classifier
Signal Processing
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Proximal support vector machine using local information
Neurocomputing
A proximal classifier with consistency
Knowledge-Based Systems
| Hi-index | 12.05 |
We first propose Distance Difference GEPSVM (DGEPSVM), a binary classifier that obtains two nonparallel planes by solving two standard eigenvalue problems. Compared with GEPSVM, this algorithm does not need to care about the singularity occurring in GEPSVM, but with better classification correctness. This formulation is capable of dealing with XOR problems with different distribution for keeping the genuine geometrical interpretation of primal GEPSVM. Moreover, the proposed algorithm gives classification correctness comparable to that of LSTSVM and TWSVM, but with lesser unknown parameters. Then, the regularization techniques are incorporated to the TWSVM. With the help of the regularized formulation, a linear programming formation for TWSVM is proposed, called FETSVM, to improve TWSVM sparsity, thereby suppressing input features. This means FETSVM is capable of reducing the number of input features, for linear case. When a nonlinear classifier is used, this means few kernel functions determine the classifier. Lastly, this algorithm is compared on artificial and public datasets. To further illustrate the effectiveness of our proposed algorithms, we also apply these algorithms to USPS handwritten digits.