A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Sparse Online Greedy Support Vector Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Soft computing for greenhouse climate control
IEEE Transactions on Fuzzy Systems
AdaBoost classifiers for pecan defect classification
Computers and Electronics in Agriculture
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The greenhouse environment is an uncertain nonlinear system which classical modeling methods cannot solve. Support vector machines regression (SVMR) is well supported by mathematical theory and has a simple structure, good generalization ability, and nonlinear modeling properties. Therefore, SVMR offers a very competent method for modeling the greenhouse environment. However, to deal with uncertainty, the model must be rectified online, and Online Sparse Least-Squares Support Vector Machines Regression (OS_LSSVMR) was developed to solve this problem. OS_LSSVMR reduced the number of training samples through use of a sample dictionary, and consequently LSSVMR has sparse solutions; the training samples were added sequentially, so that OS_LSSVMR has online learning capability. A simplified greenhouse model, in which only greenhouse internal and external air temperatures were considered, was presented, after analyzing the factors in the greenhouse environment. Then the OS_LSSVMR greenhouse model was constructed using real-world data. The resulting model shows a promising performance in the greenhouse environment, with potential improvements if a more complete data setup is used.