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

Models, called Genetic Algorithms (GA), that mimic the biological evolution process for search, optimization and machine learning. Because of their joint generic name: “;soft-computing”. A Genetic evaluated with the help of some functions, representing the constraints of the problem. The model produced by support vector classification (as described above) only depends on a subset of the training data, because the cost function for building the model does not care about training points that lie beyond the margin. In effect, the role model for Soft computing is the human mind. To introduce the ideas of fuzzy sets, fuzzy logic and use of heuristics based on human experience Adaptive Neuro-Fuzzy Inference Systems – Architecture – Hybrid Learning Algorithm – Learning Methods that Cross-fertilize ANFIS and RBFN – Coactive Neuro Fuzzy Modeling – Framework Neuron Functions for Adaptive Networks – Neuro Fuzzy Spectrum. The principal constituents, i.e., tools, techniques, of Soft Computing (SC) are – Fuzzy Logic (FL), Neural Networks (NN), Support Vector Machines (SVM), Evolutionary Computation ( EC), and – Machine Learning (ML) and Probabilistic Reasoning (PR). Biologically inspired recurrent neural networks are computationally intensive models that make extensive use of memory and numerical integration methods to calculate neural dynamics and synaptic changes. 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. Thorough introduction to the field of learning from experimental data and soft computing. 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. 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. Currently, Genetic Algorithms is used along with neural networks and fuzzy logic for solving more complex problems. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley, N.Y., 1989.