Determinant Maximization with Linear Matrix Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Approximate minimum enclosing balls in high dimensions using core-sets
Journal of Experimental Algorithmics (JEA)
Computation of Minimum-Volume Covering Ellipsoids
Operations Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Comparative study of extreme learning machine and support vector machine
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Optimum Neural Network Construction Via Linear Programming Minimum Sphere Set Covering
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
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A novel machine learning paradigm, i.e. enclosing machine learning based on regular geometric shapes was proposed. It adopted regular minimum volume enclosing and bounding geometric shapes (sphere, ellipsoid, and box) or their unions and so on to obtain one class description model and thus imitate the human "Cognizing" process. A point detection and assignment algorithm based on the one class description model was presented to imitate the human "Recognizing" process. To illustrate the concept and algorithm, a minimum volume enclosing ellipsoid (MVEE) strategy for enclosing machine learning was investigated in detail. A regularized minimum volume enclosing ellipsoid problem and dual form were presented due to probable existence of zero eigenvalues in regular MVEE problem. To solve the high dimensional one class description problem, the MVEE in kernel defined feature space was presented. A corresponding dual form and kernelized Mahalanobis distance formula was presented. We investigated the performance of the enclosing learning machine via benchmark datasets and compared with support vector machines (SVM).