A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Neural Networks - 2005 Special issue: IJCNN 2005
Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Training Recurrent Networks by Evolino
Neural Computation
A particle swarm pattern search method for bound constrained global optimization
Journal of Global Optimization
Construction and Application of PSO-SVM Model for Personal Credit Scoring
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Dynamic Distance-Based Active Learning with SVM
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Reliability forecasting by recurrent Support Vector Regression
International Journal of Artificial Intelligence and Soft Computing
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 02
Semantic analysis of real-world images using support vector machine
Expert Systems with Applications: An International Journal
Regularized Recurrent Least Squares Support Vector Machines
IJCBS '09 Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing
Ranking and selecting terms for text categorization via SVM discriminate boundary
International Journal of Intelligent Systems - Granular Computing: Models and Applications
A method to sparsify the solution of support vector regression
Neural Computing and Applications
Optimized fixed-size kernel models for large data sets
Computational Statistics & Data Analysis
Recursive Bayesian recurrent neural networks for time-series modeling
IEEE Transactions on Neural Networks
Tax forecasting theory and model based on SVM optimized by PSO
Expert Systems with Applications: An International Journal
Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification
IEEE Transactions on Image Processing
New results on recurrent network training: unifying the algorithms and accelerating convergence
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Optimization of self-organizing polynomial neural networks
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
A method for the sparse solution of recurrent support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity and allows the user to adjust the complexity of the resulting model. The sparse representation is guaranteed by limiting the number of training data points for the support vector regression method. Each training data point is selected based on the accuracy of the fully recurrent model using the active learning principle applied to the successive time-domain data. The user can adjust the training time by selecting how often the hyper-parameters of the algorithm should be optimised. The advantages of the proposed method are illustrated on several examples, and the experiments clearly show that it is possible to reduce the number of support vectors and to significantly improve the accuracy versus complexity of recurrent support vector regression machines.