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

EAN (ISBN-13): 9783642067969
ISBN (ISBN-10): 3642067964
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2010
Herausgeber: Jin, Yaochu, Springer
676 Seiten
Gewicht: 1,005 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2012-03-01T10:06:16+01:00 (Vienna)
Detailseite zuletzt geändert am 2024-02-12T13:09:46+01:00 (Vienna)
ISBN/EAN: 3642067964

ISBN - alternative Schreibweisen:
3-642-06796-4, 978-3-642-06796-9
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: jin, yao
Titel des Buches: machine learning, objective

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Daten vom Verlag:

Autor/in: Yaochu Jin
Titel: Studies in Computational Intelligence; Multi-Objective Machine Learning
Verlag: Springer; Springer Berlin
660 Seiten
Erscheinungsjahr: 2010-11-22
Berlin; Heidelberg; DE
Gedruckt / Hergestellt in Niederlande.
Gewicht: 1,021 kg
Sprache: Englisch
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.