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

Fuzzy Systems, fuzzy logic and possibility theory Computational economics. Intelligent Control and Automation (but not limited to): Mathematical modeling and analysis of complex systems. 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. 12th EANN / 7th AIAI Joint Congress 2011 : 12th (IEEE-INNS) Engineering Applications of Neural Networks / 7th (IFIP) Artificial Intelligence Applications and Innovations. 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. €� Neural networks and fuzzy logic. Learning And Soft Computing - Support Vector Machines, Neural Networks, And Fuzzy Logic Models - Vojislav Kecman.pdf. Libet-Free-Will.pdf McGraw Hill - The Modeling-Bounded-Rationality-Ariel-Rubinstein.pdf. Thorough introduction to the field of learning from experimental data and soft computing. €� Parallel algorithms Signaling and computation in biomedical data engineering. Learning-and-Soft-Computing-Support (Vector-Machines-Neural-Networks-and-Fuzzy-Logic).pdf. Implementation issues of neural networks. €� Stochastic control and filtering. €� Soft computing and control. Support Vector Machines Neural network applications. Lisp - A Practical Theory of Programming - Eric C.R. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. €� Numerical analysis and scientific computing. €� Optimization and optimal control.