Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which these modules can be combined. No human expert can claim to explore and understand all possibilities in the knowledge discovery process. This is where algorithms that learn how to learnl come to rescue. Operating in the space of all available data transformations and optimization techniques these algorithms use meta-knowledge about learning processes automatically extracted from experience of solving diverse problems. Inferences about transformations useful in different contexts help to construct learning algorithms that can uncover various aspects of knowledge hidden in the data. Meta-learning shifts the focus of the whole CI field from individual learning algorithms to the higher level of learning how to learn. This book defines and reveals new theoretical and practical trends in meta-learning, inspiring the readers to further research in this exciting field.

In recent years computational intelligence has been extended by adding many other subdisciplines and this new field requires a series of challenging problems that will give it a sense of direction in order to ensure that research efforts are not wasted. This book written by top experts in computational intelligence provides such clear directions and a much-needed focus on the most important and challenging research issues.

The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014. The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.

Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which these modules can be combined. No human expert can claim to explore and understand all possibilities in the knowledge discovery process. This is where algorithms that learn how to learnl come to rescue. Operating in the space of all available data transformations and optimization techniques these algorithms use meta-knowledge about learning processes automatically extracted from experience of solving diverse problems. Inferences about transformations useful in different contexts help to construct learning algorithms that can uncover various aspects of knowledge hidden in the data. Meta-learning shifts the focus of the whole CI field from individual learning algorithms to the higher level of learning how to learn. This book defines and reveals new theoretical and practical trends in meta-learning, inspiring the readers to further research in this exciting field.

The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamics; from single neurons to networks; complex firing patterns; movement and motion; from sensation to perception; object and face recognition; reinforcement learning; bayesian and echo state networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the brain; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; training and learning; inference and recognition; support vector machines; self-organizing maps and clustering; clustering, mining and exploratory analysis; bioinformatics; and time weries and forecasting.

The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamics; from single neurons to networks; complex firing patterns; movement and motion; from sensation to perception; object and face recognition; reinforcement learning; bayesian and echo state networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the brain; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; training and learning; inference and recognition; support vector machines; self-organizing maps and clustering; clustering, mining and exploratory analysis; bioinformatics; and time weries and forecasting.

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.

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.

This two volume set LNCS 4668 and LNCS 4669 constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007, held in Porto, Portugal, in September 2007.

The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The second volume contains 99 contributions related to computational neuroscience, neurocognitive studies, applications in biomedicine and bioinformatics, pattern recognition, data clustering, self-organization, text mining and internet applications, signal and times series processing, vision and image processing, robotics, control, real world applications, independent component analysis, graphs, emotion and attention: empirical findings and neural models, as well as understanding and creating cognitive systems.

This two volume set LNCS 4668 and LNCS 4669 constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007, held in Porto, Portugal, in September 2007.

The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The 98 papers of the first volume are organized in topical sections on learning theory, advances in neural network learning methods, ensemble learning, spiking neural networks, advances in neural network architectures neural network technologies, neural dynamics and complex systems, data analysis, estimation, spatial and spatio-temporal learning, evolutionary computing, meta learning, agents learning, complex-valued neural networks, as well as temporal synchronization and nonlinear dynamics in neural networks.

The two volume set LNCS 4131 and LNCS 4132 constitutes the refereed proceedings of the 16th International Conference on Artificial Neural Networks, ICANN 2006, held in Athens, Greece, in September 2006.

The 208 revised full papers presented were carefully reviewed and selected from 475 submissions. The 103 papers of the first volume are organized in topical sections on feature selection and dimension reduction for regression, learning algorithms, advances in neural network learning methods, ensemble learning, learning random neural networks and stochastic agents, hybrid architectures, self organization, connectionist cognitive science, cognitive machines, neural dynamics and complex systems, computational neuroscience, neural control, reinforcement learning and robotics applications, robotics, control, planning, as well as bio-inspired neural network on-chip implementation and applications. The second volume contains 105 contributions related to neural networks, semantic web technologies and multimedia analysis, bridging the semantic gap in multimedia machine learning approaches, signal and time series processing, data analysis, pattern recognition, visual attention algorithms and architectures for perceptional understanding and video coding, vision and image processing, computational finance and economics, neural computing in energy engineering, applications to biomedicine and bioinformatics, applications to security and market analysis, as well as real world applications.

The two volume set LNCS 4131 and LNCS 4132 constitutes the refereed proceedings of the 16th International Conference on Artificial Neural Networks, ICANN 2006, held in Athens, Greece, in September 2006.

The 208 revised full papers presented were carefully reviewed and selected from 475 submissions. The 103 papers of the first volume are organized in topical sections on feature selection and dimension reduction for regression, learning algorithms, advances in neural network learning methods, ensemble learning, learning random neural networks and stochastic agents, hybrid architectures, self organization, connectionist cognitive science, cognitive machines, neural dynamics and complex systems, computational neuroscience, neural control, reinforcement learning and robotics applications, robotics, control, planning, as well as bio-inspired neural network on-chip implementation and applications. The second volume contains 105 contributions related to neural networks, semantic web technologies and multimedia analysis, bridging the semantic gap in multimedia machine learning approaches, signal and time series processing, data analysis, pattern recognition, visual attention algorithms and architectures for perceptional understanding and video coding, vision and image processing, computational finance and economics, neural computing in energy engineering, applications to biomedicine and bioinformatics, applications to security and market analysis, as well as real world applications.

This volume is the first part of the two-volume proceedings of the International C- ference on Artificial Neural Networks (ICANN 2005), held on September 11-15, 2005 in Warsaw, Poland, with several accompanying workshops held on September 15, 2005 at the Nicolaus Copernicus University, Toru , Poland. The ICANN conference is an annual meeting organized by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society, and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas. The ICANN series of conferences was initiated in 1991 and soon became the major European gathering for experts in those fields. In 2005 the ICANN conference was organized by the Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, and the Nicolaus Copernicus Univ- sity, Toru , Poland. From over 600 papers submitted to the regular sessions and some 10 special c- ference sessions, the International Program Committee selected - after a thorough peer-review process - about 270 papers for publication. The large number of papers accepted is certainly a proof of the vitality and attractiveness of the field of artificial neural networks, but it also shows a strong interest in the ICANN conferences.

This volume is the first part of the two-volume proceedings of the International C- ference on Artificial Neural Networks (ICANN 2005), held on September 11-15, 2005 in Warsaw, Poland, with several accompanying workshops held on September 15, 2005 at the Nicolaus Copernicus University, Toru , Poland. The ICANN conference is an annual meeting organized by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society, and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas. The ICANN series of conferences was initiated in 1991 and soon became the major European gathering for experts in those fields. In 2005 the ICANN conference was organized by the Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, and the Nicolaus Copernicus Univ- sity, Toru , Poland. From over 600 papers submitted to the regular sessions and some 10 special c- ference sessions, the International Program Committee selected - after a thorough peer-review process - about 270 papers for publication. The large number of papers accepted is certainly a proof of the vitality and attractiveness of the field of artificial neural networks, but it also shows a strong interest in the ICANN conferences.

The purpose of these notes is to give some simple tools and pictures to physicists and ' chemists working on the many-body problem. Abstract thinking and seeing have much in common - we say "I see" meaning "I understand" , for example. Most of us prefer to have a picture of an abstract object. The remarkable popularity of the Feynman diagrams, and other diagrammatic approaches to many-body problem derived thereof, may be partially due to this preference. Yet, paradoxically, the concept of a linear space, as fundamental to quantum physics as it is, has never been cast in a graphical form. We know that is a high-order contribution to a two-particle scattering process (this one invented by Cvitanovic(1984)) corresponding to a complicated matrix element. The lines in such diagrams are labeled by indices of single-particle states. When things get complicated at this level it should be good to take a global view from the perspective of the whole many-particle space. But how to visualize the space of all many-particle states ? Methods of such visualization or graphical representation of the ,spaces of interest to physicists and chemists are the main topic of this work.