Temporal Data Mining via Unsupervised Ensemble Learning, Yang Yun

Автор: Oleg Okun
Название: Applications of Supervised and Unsupervised Ensemble Methods
ISBN: 3642039987 ISBN-13(EAN): 9783642039980
Издательство: Springer
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Цена: 20962.00 р.
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Описание: Expanding upon presentations at last year`s SUEMA (Supervised and Unsupervised Ensemble Methods and Applications) meeting, this volume explores recent developments in the field. Useful examples act as a guide for practitioners in computational intelligence.

Автор: Bruno Baruque
Название: Fusion Methods for Unsupervised Learning Ensembles
ISBN: 3642423280 ISBN-13(EAN): 9783642423284
Издательство: Springer
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Цена: 18167.00 р.
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Описание: 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.

Автор: Douzal-Chouakria
Название: Advanced Analysis and Learning on Temporal Data
ISBN: 3319444115 ISBN-13(EAN): 9783319444116
Издательство: Springer
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Цена: 5870.00 р.
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Описание: The last part of the book is dedicated to metric learning and time series comparison, it addresses the problem of speeding-up the dynamic time warping or dealing with multi-modal and multi-scale metric learning for time series classification and clustering.

Автор: Oleg Okun
Название: Supervised and Unsupervised Ensemble Methods and their Applications
ISBN: 3540789804 ISBN-13(EAN): 9783540789802
Издательство: Springer
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Цена: 20962.00 р.
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Описание: Ensembles of Clustering Methods and Their Applications.- Cluster Ensemble Methods: from Single Clusterings to Combined Solutions.- Random Subspace Ensembles for Clustering Categorical Data.- Ensemble Clustering with a Fuzzy Approach.- Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysis.- Ensembles of Classification Methods and Their Applications.- Intrusion Detection in Computer Systems Using Multiple Classifier Systems.- Ensembles of Nearest Neighbors for Gene Expression Based Cancer Classification.- Multivariate Time Series Classification via Stacking of Univariate Classifiers.- Gradient Boosting GARCH and Neural Networks for Time Series Prediction.- Cascading with VDM and Binary Decision Trees for Nominal Data.- Erratum.