Neurocomputing: foundations of research
The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Classifiers that approximate functions
Natural Computing: an international journal
Sparse Online Greedy Support Vector Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Accurate on-line support vector regression
Neural Computation
A tutorial on support vector regression
Statistics and Computing
Extending XCSF beyond linear approximation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
Classifier fitness based on accuracy
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Recursive least squares and quadratic prediction in continuous multistep problems
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Analysis and Improvements of the Classifier Error Estimate in XCSF
Learning Classifier Systems
Evolving Classifiers Ensembles with Heterogeneous Predictors
Learning Classifier Systems
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In this paper we introduce XCSF with support vector prediction:the problem of learning the prediction function is solved as a support vector regression problem and each classifier exploits a Support Vector Machine to compute the prediction. In XCSF with support vector prediction, XCSFsvm, the genetic algorithm adapts classifier conditions, classifier actions, and the SVM kernel parameters.We compare XCSF with support vector prediction to XCSF with linear prediction on the approximation of four test functions.Our results suggest that XCSF with support vector prediction compared to XCSF with linear prediction (i) is able to evolve accurate approximations of more difficult functions, (ii) has better generalization capabilities and (iii) learns faster.