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Emerging Paradigms in Machine Learning

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Feldname	Details
Vorliegende Sprache	eng
Hinweise auf parallele Ausgaben	371003555 Buchausg. u.d.T.: ‡Emerging paradigms in machine learning
ISBN	978-3-642-28698-8
Name	Ramanna, Sheela
Name	Jain, L. C.
ANZEIGE DER KETTE	Jain, L. C.
Name	Howlett, Robert J.
T I T E L	Emerging Paradigms in Machine Learning
Verlagsort	Berlin, Heidelberg
Verlag	Springer
Erscheinungsjahr	2013
Erscheinungsjahr	2013
Umfang	Online-Ressource (XXII, 498 p. 167 illus, digital)
Reihe	Smart Innovation, Systems and Technologies ; 13
Notiz / Fußnoten	Description based upon print version of record
Weiterer Inhalt	Title; Preface; Contents; Emerging Paradigms in Machine Learning: An Introduction; Introduction; Chapters of the Book; Concluding Remarks; References; Part AFoundations; Extensions of Dynamic Programming as a NewTool for Decision Tree Optimization; Introduction; Basic Notions; Decision Tables and Trees; Cost Functions; Representation of Sets of α-Decision Trees and DecisionTrees; Optimization of α-Decision Trees; Proper Subgraphs of Graph Δ (T); Procedure of Optimization; Possibilities of Sequential Optimization; Experimental Results. Relationships between Depth and Numberof MisclassificationsComputing the Relationships; Experimental Results; Conclusions; References; Optimised Information Abstraction in GranularMin/Max Clustering; Introductory Comments; Granular Information in Systems Modeling; Information Density Based Granulation; Granular Representatives of Data; Granular Refinement of Prototypes; Conclusions; References; Mining Incomplete Data-A RoughSet Approach; Introduction; Blocks of Attribute-Value Pairs; Approximations; Two Algorithms; Global MLEM2; Local MLEM2; Incomplete Data Sets with Numerical Attributes. ExperimentsConclusions; References; Roles Played by Bayesian Networks in MachineLearning: An Empirical Investigation; Introduction; Relevant Concepts Related to Bayesian Networks and Bayesian Classifiers; Learning Bayesian Networks and Bayesian Classifiers from Data; Bayesian Classifiers in Feature Subset Selection; Bayesian Classifiers in Imputation Processes; Post-processing a Bayesian Classifier into a Set of Rules; Conclusion; References; Evolving Intelligent Systems: Methods,Algorithms and Applications; Introduction; Evolving Fuzzy Systems; Evolving Takagi-Sugeno (eTS). Other Evolving Fuzzy ModelsEvolving Multivariable Gaussian; Gaussian Participatory Evolving Clustering; Evolving Multivariable Gaussian Fuzzy Model; Evolving Fuzzy Linear Regression Trees; Fuzzy Linear Regression Trees; Incremental Learning Algorithm; Experiments; Short Term Electricity Load Forecasting; Tree Rings; Conclusion; References; Emerging Trends in MachineLearning: Classification of Stochastically Episodic Events; Introduction; Problem Formulation; SE Event Recognition; Characteristics of the Domain of Problems; Overview of Our Solution; Pattern Recognition: State of the Art. Supervised LearningAlternative Learning Paradigms; Sampling; Dynamic Classification; Modelling the Problem; Application Domain; Procuring Data: Aspects of Simulation; Generated Datasets; PR Solutions; Classification Scenarios; Classification; Classifier Assessment Criteria; Results: Scenario 1; General Performance; Performance on Short- and Long-Range Detonations; Performance as a Function of Distance; Expanded Feature-Space; Results: Scenario 2; General Performance; Performance on Short- and Long-RangeDetonations; Performance as a Function of Distance; Expanded Feature-Space; Discussion. Results: S1
Titelhinweis	Buchausg. u.d.T.: ‡Emerging paradigms in machine learning
ISBN	ISBN 978-3-642-28699-5
Klassifikation	UYQ
	COM004000
	*68-06
	00B15
	68T05
	006.3
	Q342
	ST 300
	ST 302
Kurzbeschreibung	This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.
1. Schlagwortkette	Maschinelles Lernen
	Aufsatzsammlung
	Aufsatzsammlung
ANZEIGE DER KETTE	Maschinelles Lernen -- Aufsatzsammlung -- Aufsatzsammlung
SWB-Titel-Idn	373364946
Signatur	Springer E-Book
Bemerkungen	Elektronischer Volltext - Campuslizenz
Elektronische Adresse	$uhttp://dx.doi.org/10.1007/978-3-642-28699-5
Internetseite / Link	Volltext
Siehe auch	Inhaltsverzeichnis
Siehe auch	Inhaltstext
Siehe auch	Volltext
Siehe auch	Cover
Siehe auch	Inhaltstext

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