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Books > Computing & IT > Applications of computing > Artificial intelligence
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Fusion Methods for Unsupervised Learning Ensembles (Hardcover, 2011 ed.)
Loot Price: R3,251
Discovery Miles 32 510
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Fusion Methods for Unsupervised Learning Ensembles (Hardcover, 2011 ed.)
Series: Studies in Computational Intelligence, 322
Expected to ship within 10 - 15 working days
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The application of a "committee of experts" or ensemble learning to
artificial neural networks that apply unsupervised learning
techniques is widely considered to enhance the effectiveness of
such networks greatly. This book examines the potential of the
ensemble meta-algorithm by describing and testing a technique based
on the combination of ensembles and statistical PCA that is able to
determine the presence of outliers in high-dimensional data sets
and to minimize outlier effects in the final results. Its central
contribution concerns an algorithm for the ensemble fusion of
topology-preserving maps, referred to as Weighted Voting
Superposition (WeVoS), which has been devised to improve data
exploration by 2-D visualization over multi-dimensional data sets.
This generic algorithm is applied in combination with several other
models taken from the family of topology preserving maps, such as
the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for
topology preserving maps that are proposed in the literature are
used to validate and compare WeVoS with other algorithms. The
experimental results demonstrate that, in the majority of cases,
the WeVoS algorithm outperforms earlier map-fusion methods and the
simpler versions of the algorithm with which it is compared. All
the algorithms are tested in different artificial data sets and in
several of the most common machine-learning data sets in order to
corroborate their theoretical properties. Moreover, a real-life
case-study taken from the food industry demonstrates the practical
benefits of their application to more complex problems.
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