Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Accurate on-line support vector regression
Neural Computation
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Geometrical multi-resolution network based on ridgelet frame
Signal Processing
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
Neurocomputing
The Forgetron: A Kernel-Based Perceptron on a Budget
SIAM Journal on Computing
Incremental constructive ridgelet neural network
Neurocomputing
Neurocomputing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Visualization and Classification of Power System Frequency Data Streams
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Tracking the best hyperplane with a simple budget perceptron
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Support vector machine techniques for nonlinear equalization
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
RKHS approach to detection and estimation problems--V: Parameter estimation
IEEE Transactions on Information Theory
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
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In this paper, a Sparse Ridgelet Kernel Regressor (SRKR) is constructed by combing the ridgelet theory, the sparse theory with kernel trick. By using the dimensionality non-separable ridgelet kernels, SRKR is capable of processing the high-dimensional data more efficiently. Considering the preferable future of sequential learning over batch learning in problems where data arrive constantly and where batch learning is expensive, we exploit the new kernel method in an online setting using the sequential extreme learning scheme. The online learning algorithm of the examples, named Online Sequential Extreme Learning Algorithm (OS-ELA) is employed to rapidly produce a sequence of estimations. OS-ELA learns the training data one-by-one or chunk by chunk (with fixed or varying size), and discards them as long as the training procedure for those data is completed to keep the memory bounded in online learning. Evolution scheme is also incorporated to obtain a 'good' sparse regressor. Experiments are taken on some nonlinear time-series prediction problems, in which the examples are available one by one. Some comparisons are made and the experimental results show its efficiency and superiority to its counterparts.