Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Kernel Matrix Learning for One-Class Classification
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Coresets for polytope distance
Proceedings of the twenty-fifth annual symposium on Computational geometry
Brief paper: On-line voltage security assessment of power systems using core vector machines
Engineering Applications of Artificial Intelligence
From minimum enclosing ball to fast fuzzy inference system training on large datasets
IEEE Transactions on Fuzzy Systems
An online core vector machine with adaptive MEB adjustment
Pattern Recognition
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
ACM Transactions on Algorithms (TALG)
Fast support vector data descriptions for novelty detection
IEEE Transactions on Neural Networks
A robust fuzzy rough set model based on minimum enclosing ball
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Condensed vector machines: learning fast machine for large data
IEEE Transactions on Neural Networks
Ordinal-class core vector machine
Journal of Computer Science and Technology
A new algorithm for training SVMs using approximate minimal enclosing balls
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Expert Systems with Applications: An International Journal
Two one-pass algorithms for data stream classification using approximate MEBs
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Using the leader algorithm with support vector machines for large data sets
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Rough margin based core vector machine
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Expert Systems with Applications: An International Journal
Soft Minimum-Enclosing-Ball Based Robust Fuzzy Rough Sets
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Fast multi-label core vector machine
Pattern Recognition
Training sparse SVM on the core sets of fitting-planes
Neurocomputing
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Kernel methods, such as the support vector machine (SVM), are often formulated as quadratic programming (QP) problems. However, given m training patterns, a naive implementation of the QP solver takes O(m 3) training time and at least O(m2) space. Hence, scaling up these QPs is a major stumbling block in applying kernel methods on very large data sets, and a replacement of the naive method for finding the QP solutions is highly desirable. Recently, by using approximation algorithms for the minimum enclosing ball (MEB) problem, we proposed the core vector machine (CVM) algorithm that is much faster and can handle much larger data sets than existing SVM implementations. However, the CVM can only be used with certain kernel functions and kernel methods. For example, the very popular support vector regression (SVR) cannot be used with the CVM. In this paper, we introduce the center-constrained MEB problem and subsequently extend the CVM algorithm. The generalized CVM algorithm can now be used with any linear/nonlinear kernel and can also be applied to kernel methods such as SVR and the ranking SVM. Moreover, like the original CVM, its asymptotic time complexity is again linear in m and its space complexity is independent of m. Experiments show that the generalized CVM has comparable performance with state-of-the-art SVM and SVR implementations, but is faster and produces fewer support vectors on very large data sets