Wlodzislaw Duch – författare
Artificial Neural Networks and Machine Learning -- ICANN 2014
24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014, Proceedings
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Challenges for Computational Intelligence
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In the year 1900 at the International Congress of Mathematicians in Paris David Hilbert delivered what is now considered the most important talk ever given in the history of mathematics, proposing 23 major problems worth working at in the future. One hundred years later the impact of this talk is still strong: some problems have been solved, new problems have been added, but the direction once set - identify the most important problems and focus on them - is still actual.
Computational Intelligence (CI) is used as a name to cover many existing branches of science, with artificial neural networks, fuzzy systems and evolutionary computation forming its core. In recent years CI has been extended by adding many other subdisciplines and it became quite obvious that this new field also requires a series of challenging problems that will give it a sense of direction. Without setting up clear goals and yardsticks to measure progress on the way many research efforts are wasted.
The book written by top experts in CI provides such clear directions and the much-needed focus on the most important and challenging research issues, showing a roadmap how to achieve ambitious goals.
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Challenges for Computational Intelligence
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Meta-Learning in Computational Intelligence
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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.
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Meta-Learning in Computational Intelligence
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566 kr
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