Model Induction with Support Vector Machines: Introduction and Applications
| Autor: | Dimitri Solomatine, Slavco Velickov, Michael B. Abbott, Yonas Dibike |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
Artificial neural network
Computer science business.industry Evolutionary algorithm Decision tree Intelligent decision support system Online machine learning Machine learning computer.software_genre Computer Science Applications Support vector machine Information system Structural risk minimization Artificial intelligence business computer Civil and Structural Engineering |
| Zdroj: | Journal of Computing in Civil Engineering. 15:208-216 |
| ISSN: | 1943-5487 0887-3801 |
| DOI: | 10.1061/(asce)0887-3801(2001)15:3(208) |
| Popis: | The rapid advance in information processing systems in recent decades had directed engineering research towards the development of intelligent systems that can evolve models of natural phenomena automatically—“by themselves,” so to speak. In this respect, a wide range of machine learning techniques like decision trees, artificial neural networks (ANNs), Bayesian methods, fuzzy-rule based systems, and evolutionary algorithms have been successfully applied to model different civil engineering systems. In this study, the possibility of using yet another machine learning paradigm that is firmly based on the theory of statistical learning, namely that of the support vector machine (SVM), is investigated. An interesting property of this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over the data set. In this paper, the basic ... |
| Databáze: | OpenAIRE |
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