Meta-Learning in Computational Intelligence [electronic resource] /
Jankowski, Norbert.editor.
Duch, Włodzisław.editor.
Grąbczewski, Krzysztof.editor.
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.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
Universal meta-learning architecture and algorithms -- Meta-learning of instance selection for data summarization -- Choosing the metric: a simple model approach -- Meta-learning Architectures: Collecting, Organizing and Exploiting Meta-knowledge -- Computational intelligence for meta-learning: a promising avenue of research -- Self-organization of supervised models -- Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach -- A Meta-Model Perspective and Attribute Grammar Approach to Facilitating the Development of Novel Neural Network Models -- Ontology-Based Meta-Mining of Knowledge Discovery Workflows -- Optimal Support Features for Meta-learning.
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.
Artificial intelligence.
Computational Intelligence.
Artificial Intelligence.
Studies in Computational Intelligence