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2010, ISBN: 9783642067969

Springer, Taschenbuch, Auflage: Softcover reprint of hardcover 1st ed. 2006, 676 Seiten, Publiziert: 2010-11-22T00:00:01Z, Produktgruppe: Buch, Hersteller-Nr.: 254 black & white illustrat… Meer...

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Multi-Objective Machine Learning (Studies in Computational Intelligence, Band 16) - pocketboek

2010, ISBN: 9783642067969

Springer, Taschenbuch, Auflage: Softcover reprint of hardcover 1st ed. 2006, 676 Seiten, Publiziert: 2010-11-22T00:00:01Z, Produktgruppe: Buch, Hersteller-Nr.: 254 black & white illustrat… Meer...

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Multi-Objective Machine Learning (Studies in Computational Intelligence, Band 16) - pocketboek

2010

ISBN: 9783642067969

Springer, Taschenbuch, Auflage: Softcover reprint of hardcover 1st ed. 2006, 676 Seiten, Publiziert: 2010-11-22T00:00:01Z, Produktgruppe: Buch, Hersteller-Nr.: 254 black & white illustrat… Meer...

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Multi-Objective Machine Learning Softcover reprint of hardcover 1st ed. 2006 - pocketboek

2010, ISBN: 9783642067969

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Jin, Yaochu:
Multi-Objective Machine Learning - pocketboek

2010, ISBN: 9783642067969

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Multi-Objective Machine Learning (Studies in Computational Intelligence, Band 16)

Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.

Gedetalleerde informatie over het boek. - Multi-Objective Machine Learning (Studies in Computational Intelligence, Band 16)


EAN (ISBN-13): 9783642067969
ISBN (ISBN-10): 3642067964
Gebonden uitgave
pocket book
Verschijningsjaar: 2010
Uitgever: Jin, Yaochu, Springer
676 Bladzijden
Gewicht: 1,005 kg
Taal: eng/Englisch

Boek bevindt zich in het datenbestand sinds 2012-03-01T10:06:16+01:00 (Amsterdam)
Detailpagina laatst gewijzigd op 2024-02-12T13:09:46+01:00 (Amsterdam)
ISBN/EAN: 9783642067969

ISBN - alternatieve schrijfwijzen:
3-642-06796-4, 978-3-642-06796-9
alternatieve schrijfwijzen en verwante zoekwoorden:
Auteur van het boek: jin, yao
Titel van het boek: machine learning, objective


Gegevens van de uitgever

Auteur: Yaochu Jin
Titel: Studies in Computational Intelligence; Multi-Objective Machine Learning
Uitgeverij: Springer; Springer Berlin
660 Bladzijden
Verschijningsjaar: 2010-11-22
Berlin; Heidelberg; DE
Gedrukt / Gemaakt in
Gewicht: 1,021 kg
Taal: Engels
213,99 € (DE)
219,99 € (AT)
236,00 CHF (CH)
POD
XIV, 660 p. 254 illus.

BC; Mathematical and Computational Engineering; Hardcover, Softcover / Technik/Allgemeines, Lexika; Mathematik für Ingenieure; Verstehen; Support Vector Machine; decision tree; evolution; fuzzy; fuzzy system; fuzzy systems; genetic algorithms; intelligent systems; learning; machine learning; model; multi-objective optimization; neural network; neural networks; optimization; Artificial Intelligence; Complex Systems; Statistical Physics and Dynamical Systems; Mathematical and Computational Engineering Applications; Artificial Intelligence; Complex Systems; Theoretical, Mathematical and Computational Physics; Künstliche Intelligenz; Kybernetik und Systemtheorie; Mathematische Physik; BB

Multi-Objective Clustering, Feature Extraction and Feature Selection.- Feature Selection Using Rough Sets.- Multi-Objective Clustering and Cluster Validation.- Feature Selection for Ensembles Using the Multi-Objective Optimization Approach.- Feature Extraction Using Multi-Objective Genetic Programming.- Multi-Objective Learning for Accuracy Improvement.- Regression Error Characteristic Optimisation of Non-Linear Models.- Regularization for Parameter Identification Using Multi-Objective Optimization.- Multi-Objective Algorithms for Neural Networks Learning.- Generating Support Vector Machines Using Multi-Objective Optimization and Goal Programming.- Multi-Objective Optimization of Support Vector Machines.- Multi-Objective Evolutionary Algorithm for Radial Basis Function Neural Network Design.- Minimizing Structural Risk on Decision Tree Classification.- Multi-objective Learning Classifier Systems.- Multi-Objective Learning for Interpretability Improvement.- Simultaneous Generation of Accurate and Interpretable Neural Network Classifiers.- GA-Based Pareto Optimization for Rule Extraction from Neural Networks.- Agent Based Multi-Objective Approach to Generating Interpretable Fuzzy Systems.- Multi-objective Evolutionary Algorithm for Temporal Linguistic Rule Extraction.- Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model.- Multi-Objective Ensemble Generation.- Pareto-Optimal Approaches to Neuro-Ensemble Learning.- Trade-Off Between Diversity and Accuracy in Ensemble Generation.- Cooperative Coevolution of Neural Networks and Ensembles of Neural Networks.- Multi-Objective Structure Selection for RBF Networks and Its Application to Nonlinear System Identification.- Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection.- Applications of Multi-Objective Machine Learning.- Multi-Objective Optimisation for Receiver Operating Characteristic Analysis.- Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination.- Fuzzy Tuning for the Docking Maneuver Controller of an Automated Guided Vehicle.- A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments.- Multi-Objective Neural Network Optimization for Visual Object Detection.

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