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Multi-Objective Machine Learning

'Studies in Computational Intelligence'. 2006. 2006. 254 schwarz-weiße Abbildungen, 104 schwarz-weiße…
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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-objecti … weiterlesen
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Titel: Multi-Objective Machine Learning

ISBN: 3540306765
EAN: 9783540306764
'Studies in Computational Intelligence'.
2006. 2006.
254 schwarz-weiße Abbildungen, 104 schwarz-weiße Tabellen, Bibliographie.
Book.
Sprache: Englisch.
Herausgegeben von Yaochu Jin
Springer-Verlag GmbH

10. Februar 2006 - gebunden - XIII

Beschreibung

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.

Inhaltsverzeichnis

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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