A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Incremental Support Vector Machine Construction
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Exact simplification of support vector solutions
The Journal of Machine Learning Research
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Sparseness of support vector machines
The Journal of Machine Learning Research
Object Recognition Using Composed Receptive Field Histograms of Higher Dimensionality
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Predictive low-rank decomposition for kernel methods
ICML '05 Proceedings of the 22nd international conference on Machine learning
An efficient method for simplifying support vector machines
ICML '05 Proceedings of the 22nd international conference on Machine learning
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
IEEE Transactions on Signal Processing
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
An online core vector machine with adaptive MEB adjustment
Pattern Recognition
Online independent reduced least squares support vector regression
Information Sciences: an International Journal
Online learning with multiple kernels: A review
Neural Computation
Online learning algorithm of kernel-based ternary classifiers using support vectors
Optical Memory and Neural Networks
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Support vector machines (SVMs) are one of the most successful algorithms for classification. However, due to their space and time requirements, they are not suitable for on-line learning, that is, when presented with an endless stream of training observations. In this paper we propose a new on-line algorithm, called on-line independent support vector machines (OISVMs), which approximately converges to the standard SVM solution each time new observations are added; the approximation is controlled via a user-defined parameter. The method employs a set of linearly independent observations and tries to project every new observation onto the set obtained so far, dramatically reducing time and space requirements at the price of a negligible loss in accuracy. As opposed to similar algorithms, the size of the solution obtained by OISVMs is always bounded, implying a bounded testing time. These statements are supported by extensive experiments on standard benchmark databases as well as on two real-world applications, namely place recognition by a mobile robot in an indoor environment and human grasping posture classification.