Meta-Learning in Computational Intelligence (Studies in Computational Intelligence, 358)

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. "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 learn 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."--Publisher's website Front Matter....Pages - Universal Meta-Learning Architecture and Algorithms....Pages 1-76 Meta-Learning of Instance Selection for Data Summarization....Pages 77-95 Choosing the Metric: A Simple Model Approach....Pages 97-115 Meta-Learning Architectures: Collecting, Organizing and Exploiting Meta-Knowledge....Pages 117-155 Computational Intelligence for Meta-Learning: A Promising Avenue of Research....Pages 157-177 Self-organization of Supervised Models....Pages 179-223 Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach....Pages 225-243 A Meta-Model Perspective and Attribute Grammar Approach to Facilitating the Development of Novel Neural Network Models....Pages 245-272 Ontology-Based Meta-Mining of Knowledge Discovery Workflows....Pages 273-315 Optimal Support Features for Meta-Learning....Pages 317-358 Back Matter....Pages -