Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Training v-support vector regression: theory and algorithms
Neural Computation
Accurate on-line support vector regression
Neural Computation
A tutorial on support vector regression
Statistics and Computing
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Investigation of evolutionary optimization methods of TSK fuzzy model for real estate appraisal
International Journal of Hybrid Intelligent Systems - Recent Advances in Intelligent Paradigms Fusion and Their Applications
The use of fuzzy logic in predicting house selling price
Expert Systems with Applications: An International Journal
Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Comparison of data driven models for the valuation of residential premises using KEEL
International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
The mass appraisal of the real estate by computational intelligence
Applied Soft Computing
On employing fuzzy modeling algorithms for the valuation of residential premises
Information Sciences: an International Journal
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
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Incremental support vector regression algorithms (SVR) and sequential minimal optimization algorithms (SMO) for regression were implemented. Intensive experiments to compare predictive accuracy of the algorithms with different kernel functions over several datasets taken from a cadastral system were conducted in offline scenario. The statistical analysis of experimental output was made employing the nonparametric methodology designed especially for multiple N×N comparisons of N algorithms over N datasets including Friedman tests followed by Nemenyi's, Holm's, Shaffer's, and Bergmann-Hommel's post-hoc procedures. The results of experiments showed that most of SVR algorithms outperformed significantly a pairwise comparison method used by the experts to estimate the values of residential premises over all datasets. Moreover, no statistically significant differences between incremental SVR and non-incremental SMO algorithms were observed using our stationary cadastral datasets. The results open the opportunity of further research into the application of incremental SVR algorithms to predict from a data stream of real estate sales/purchase transactions.