Multi-Objective Machine Learning

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 su...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Jin, Yaochu (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.
Edición:1st ed. 2006.
Colección:Studies in Computational Intelligence, 16
Temas:
Engineering mathematics.
Engineering-Data processing.
Artificial intelligence.
System theory.
Mathematical physics.
Mathematical and Computational Engineering Applications.
Artificial Intelligence.
Complex Systems.
Theoretical, Mathematical and Computational Physics.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Multi-Objective Machine Learning  |h [electronic resource] /  |c edited by Yaochu Jin. 
250 |a 1st ed. 2006. 
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490 1 |a Studies in Computational Intelligence,  |x 1860-9503 ;  |v 16 
505 0 |a 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. 
520 |a 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. 
650 0 |a Engineering mathematics. 
650 0 |a Engineering-Data processing. 
650 0 |a Artificial intelligence. 
650 0 |a System theory. 
650 0 |a Mathematical physics. 
650 1 4 |a Mathematical and Computational Engineering Applications. 
650 2 4 |a Artificial Intelligence. 
650 2 4 |a Complex Systems. 
650 2 4 |a Theoretical, Mathematical and Computational Physics. 
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