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001 978-3-642-22910-7
003 DE-He213
005 20140220083810.0
007 cr nn 008mamaa
008 110830s2011 gw | s |||| 0|eng d
020 _a9783642229107
_9978-3-642-22910-7
024 7 _a10.1007/978-3-642-22910-7
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aOkun, Oleg.
_eeditor.
245 1 0 _aEnsembles in Machine Learning Applications
_h[electronic resource] /
_cedited by Oleg Okun, Giorgio Valentini, Matteo Re.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aXX, 252 p.
_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 ;
_v373
505 0 _aFrom the content: Facial Action Unit Recognition Using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classifiers -- On the Design of Low Redundancy Error-Correcting Output Codes -- Minimally-Sized Balanced Decomposition Schemes for Multi-Class Classification -- Bias-Variance Analysis of ECOC and Bagging Using Neural Nets -- Fast-ensembles of Minimum Redundancy Feature Selection.
520 _aThis book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain). As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms – advanced machine learning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a group of algorithms, each of which first independently solves the task at hand by assigning a class or cluster label (voting) to instances in a dataset and after that all votes are combined together to produce the final class or cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems.   This book consists of 14 chapters, each of which can be read independently of the others. In addition to two previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in practice and to help to both researchers and engineers developing ensemble applications.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aValentini, Giorgio.
_eeditor.
700 1 _aRe, Matteo.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642229091
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v373
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-22910-7
912 _aZDB-2-ENG
999 _c108278
_d108278