Meta-Learning in Computational Intelligence
Series: Studies in Computational Intelligence; 358;
- Publisher's listprice EUR 235.39
-
97 628 Ft (92 979 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 19 526 Ft off)
- Discounted price 78 102 Ft (74 383 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
97 628 Ft
Availability
printed on demand
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 2011
- Publisher Springer Berlin Heidelberg
- Date of Publication 10 June 2011
- Number of Volumes 1 pieces, Book
- ISBN 9783642209796
- Binding Hardback
- No. of pages359 pages
- Size 235x155 mm
- Weight 791 g
- Language English
- Illustrations IX, 359 p. Illustrations, black & white 0
Categories
Long description:
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.
Table of Contents:
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.
More
