Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Online learning of linear classifiers
Advanced lectures on machine learning
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
A primal-dual perspective of online learning algorithms
Machine Learning
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
The Forgetron: A Kernel-Based Perceptron on a Budget
SIAM Journal on Computing
Two Algorithms for the Minimum Enclosing Ball Problem
SIAM Journal on Optimization
Streamed learning: one-pass SVMs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
An online core vector machine with adaptive MEB adjustment
Pattern Recognition
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
IEEE Transactions on Signal Processing
Generalized Core Vector Machines
IEEE Transactions on Neural Networks
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It has been recently shown that the quadratic programming formulation underlying a number of kernel methods can be treated as a minimal enclosing ball (MEB) problem in a feature space where data has been previously embedded. Core Vector Machines (CVMs) in particular, make use of this equivalence in order to compute Support Vector Machines (SVMs) from very large datasets in the batch scenario. In this paper we study two algorithms for online classification which extend this family of algorithms to deal with large data streams. Both algorithms use analytical rules to adjust the model extracted from the stream instead of recomputing the entire solution on the augmented dataset.We show that these algorithms are more accurate than the current extension of CVMs to handle data streams using an analytical rule instead of solving large quadratic programs. Experiments also show that the online approaches are considerably more efficient than periodic computation of CVMs even though warm start is being used.