Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Sparse on-line Gaussian processes
Neural Computation
Sparse Online Greedy Support Vector Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Exact simplification of support vector solutions
The Journal of Machine Learning Research
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
The Forgetron: A Kernel-Based Perceptron on a Budget
SIAM Journal on Computing
The projectron: a bounded kernel-based Perceptron
Proceedings of the 25th international conference on Machine learning
Limited stochastic meta-descent for kernel-based online learning
Neural Computation
Bounded Kernel-Based Online Learning
The Journal of Machine Learning Research
Tracking the best hyperplane with a simple budget perceptron
COLT'06 Proceedings of the 19th annual conference on Learning Theory
IEEE Transactions on Signal Processing
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
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In this study, we propose a dynamical memory strategy to efficiently control the size of the support set in a kernel-based Perceptron learning algorithm. The method consists of two operations, namely, the incremental and decremental projections. In the incremental projection, a new presented instance is either added to the support set or discarded depending on a predefined rule. To diminish information loss, we do not throw away those discarded examples cheaply, instead their impact to the discriminative function is sustained by a projection technique, which maps the modified discriminative function into the space spanned by the original support set. When a new example is added to the support set, the algorithm moves to the decremental projection. We evaluate the minimum information loss by deleting one instance from the support set. If this minimum information loss is less than a tolerable threshold, then the corresponding instance is removed, however, its contribution to the discriminative function is reserved by the projection technique. By this, our method can on one hand keep a relatively small size of the support set and on the other hand achieve a high classification accuracy. We also develop a method which sets a budget for the size of the support set. We test our approaches to four benchmark data sets, and find that our methods outperform others in either having higher classification accuracies when the sizes of their support sets are comparable or having smaller sizes of the support sets when their classification accuracies are similar.