Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models Vojislav Kecman
Publisher: The MIT Press

Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. € Optimization and optimal control. Ajith Abraham, Crina Grosan and Stefan Tigan, Ensemble of Hybrid Neural Network Learning Approaches for Designing Pharmaceutical Drugs , Neural Computing & Applications, Springer Verlag London Ltd., Volume 16, No. € Neural networks and fuzzy logic. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other. Subsequently, a theoretical analysis of these techniques is . Roselina Sallehuddin, Siti Mariyam Shamsuddin, Siti Zaiton Hashim and Ajith Abraham, Forecasting time series using hybrid grey relational artificial neural network and auto regressive integrated moving average model, Neural Network World, Volume 17, No. In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. € Soft computing and control. € Numerical analysis and scientific computing. All the papers in: Environment, Economics, Energy, Devices, Systems, Communications, Computers, Biomedicine and Mathematics accepted, registered and presented in IAASAT conferences will be eligible for publication in several ISI special .. € Stochastic control and filtering. Thereafter, different soft computing techniques like neural networks, genetic algorithms, and hybrid approaches are discussed along with their application to gene prediction. 12th EANN / 7th AIAI Joint Congress 2011 : 12th (IEEE-INNS) Engineering Applications of Neural Networks / 7th (IFIP) Artificial Intelligence Applications and Innovations. € Parallel algorithms Signaling and computation in biomedical data engineering. Learning And Soft Computing | Support Vector Machines, Neural Networks, and Fuzzy Logic Models. In this work three supervised classification methods, support vector machine (SVM), artificial neural network (ANN), and decision tree (DT), are used for classification task.