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The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature. It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Currently this method has been included in a large number of commercial and public domain software packages. In this book, top experts on the SOM method take a look at the state of the art and the future of this computing paradigm. The 30 chapters of this book cover the current status of SOM theory, such as connections of S

Artificial intelligence. Robotics. Simulation. Graphics --- Neural networks (Computer science). --- Self-organizing maps. --- Neural networks (Computer science) --- Algorithms, Kohonen --- Kohonen algorithms --- Kohonen maps --- Kohonen's maps --- Maps, Kohonen --- Maps, Self-organizing --- SOMs (Self-organizing maps) --- Self-organizing systems --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing

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Complex analysis --- Mathematical statistics --- Computer science --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- patroonherkenning --- neuronale netwerken --- beeldverwerking --- ICT (informatie- en communicatietechnieken) --- factoranalyse --- complexe analyse (wiskunde) --- machine learning --- informatica --- informatiesystemen --- KI (kunstmatige intelligentie) --- robots --- AI (artificiële intelligentie)

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In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t.

Mathematics --- Machine elements

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This two volume set (LNCS 6791 and LNCS 6792) constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.

Complex analysis --- Mathematical statistics --- Computer science --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- patroonherkenning --- neuronale netwerken --- beeldverwerking --- ICT (informatie- en communicatietechnieken) --- factoranalyse --- complexe analyse (wiskunde) --- machine learning --- informatica --- informatiesystemen --- KI (kunstmatige intelligentie) --- robots