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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 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.
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