000			03508nam a22004695i 4500
001			978-3-642-16205-3
003			DE-He213
005			20140220083748.0
007			cr nn 008mamaa
008			101117s2011 gw | s |||| 0|eng d
020			_a9783642162053 _9978-3-642-16205-3
024	7		_a10.1007/978-3-642-16205-3 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aBaruque, Bruno. _eauthor.
245	1	0	_aFusion Methods for Unsupervised Learning Ensembles _h[electronic resource] / _cby Bruno Baruque, Emilio Corchado.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_a166p. 50 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Computational Intelligence, _x1860-949X ; _v322
505	0		_a1 Introduction -- 2 Modelling Human Learning: Artificial Neural Networks -- 3 The Committee of Experts Approach: Ensemble Learning -- 4 Use of Ensembles for Outlier Overcoming -- 5 Ensembles of Topology Preserving Maps -- 6 A Novel Fusion Algorithm for Topology-Preserving Maps.-7 Conclusions.
520			_aThe 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 topologypreserving 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.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aCorchado, Emilio. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642162046
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v322
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-16205-3
912			_aZDB-2-ENG
999			_c107128 _d107128