Reproducing Polynomial Kernel Extreme Learning Machine
- 20 September 2017
- journal article
- Published by Fuji Technology Press Ltd. in Journal of Advanced Computational Intelligence and Intelligent Informatics
- Vol. 21 (5), 795-802
- https://doi.org/10.20965/jaciii.2017.p0795
Abstract
Conventional kernel support vector machine (KSVM) has the problem of slow training speed, and single kernel extreme learning machine (KELM) also has some performance limitations, for which this paper proposes a new combined KELM model that build by the polynomial kernel and reproducing kernel on Sobolev Hilbert space. This model combines the advantages of global and local kernel function and has fast training speed. At the same time, an efficient optimization algorithm called cuckoo search algorithm is adopted to avoid blindness and inaccuracy in parameter selection. Experiments were performed on bi-spiral benchmark dataset, Banana dataset, as well as a number of classification and regression datasets from the UCI benchmark repository illustrate the feasibility of the proposed model. It achieves the better robustness and generalization performance when compared to other conventional KELM and KSVM, which demonstrates its effectiveness and usefulness.Keywords
This publication has 16 references indexed in Scilit:
- Pruned Fast Learning Fuzzy Approach for Data-Driven Traffic Flow PredictionJournal of Advanced Computational Intelligence and Intelligent Informatics, 2016
- Robust Visual Knowledge Transfer via Extreme Learning Machine-Based Domain AdaptationIEEE Transactions on Image Processing, 2016
- A kernel extreme learning machine algorithm based on improved particle swam optimizationMemetic Computing, 2016
- Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose SystemsIEEE Transactions on Instrumentation and Measurement, 2014
- Extreme Learning Machine for Regression and Multiclass ClassificationIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011
- Optimization method based extreme learning machine for classificationNeurocomputing, 2010
- Kernel Canonical Discriminant Analysis Based on Variable SelectionJournal of Advanced Computational Intelligence and Intelligent Informatics, 2009
- Ternary reversible extreme learning machines: the incremental tri-training method for semi-supervised classificationKnowledge and Information Systems, 2009
- Convex incremental extreme learning machineNeurocomputing, 2007
- Prediction of Continuous Time Autoregressive Processes via the Reproducing Kernel SpacesStatistical Inference for Stochastic Processes, 2003