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04999nam a22005175i 4500 |
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|a 9783540330196
|9 978-3-540-33019-6
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|a 10.1007/3-540-33019-4
|2 doi
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|a MAT003000
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|a 519
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|a Multi-Objective Machine Learning
|h [electronic resource] /
|c edited by Yaochu Jin.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2006.
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|a XIV, 660 p. 254 illus.
|b online resource.
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|a text
|b txt
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|a computer
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|a text file
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 16
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|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.
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|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.
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|a Engineering.
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|a Artificial intelligence.
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|a Statistical physics.
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|a Dynamical systems.
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|a Applied mathematics.
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|a Engineering mathematics.
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|a Engineering.
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|a Appl.Mathematics/Computational Methods of Engineering.
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|a Artificial Intelligence (incl. Robotics).
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|a Statistical Physics, Dynamical Systems and Complexity.
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|a Jin, Yaochu.
|e editor.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783540306764
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 16
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|u http://dx.doi.org/10.1007/3-540-33019-4
|z Full Text via HEAL-Link
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|a ZDB-2-ENG
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|a Engineering (Springer-11647)
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