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

Fuzzy logic and fuzzy Unsupervised and reinforcement learning. Implementation issues of neural networks. Intelligent Control and Automation (but not limited to): Mathematical modeling and analysis of complex systems. Connectionist theory and cognitive science. Learning And Soft Computing - Support Vector Machines, Neural Networks, And Fuzzy Logic Models - Vojislav Kecman.pdf. Patrick Blackburn, Johan Bos , Kristina Striegnitz.pdf. Fuzzy systems architectures and hardware. Mathematical modeling of neural systems. Neuroinformatics Support vector machines and kernel methods. Support Vector Machines Neural network applications. (165), Masanobu Kittaka and Masafumi Hagiwara: “Language Processing Neural Network with Additional Learning,”International Conference on Soft Computing and Intelligent Systems & ISIS 2008, 2008-09. (164), Hajime Hotta, Masafumi ( 150), Hajime Hotta, Masafumi Hagiwara:“A Japanese Font Designing System Using Fuzzy-Logic-Based Kansei Database,” International Symposium on Advanced Intelligent Systems (ISIS 2005), pp.723-728, 2005-09. 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. 12th EANN / 7th AIAI Joint Congress 2011 : 12th (IEEE-INNS) Engineering Applications of Neural Networks / 7th (IFIP) Artificial Intelligence Applications and Innovations. 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. Fuzzy Systems, fuzzy logic and possibility theory Computational economics. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks.