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...
Gardado en:
| Outros autores: | |
|---|---|
| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Edición: | 1st ed. 2006. |
| Series: | Studies in Computational Intelligence
16 |
| Acceso en liña: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Multi-objective machine learning, Yaochu Jin (ed.), Berlin, Springer, 2006, 1 vol. (xiii, 660 p.), Studies in computational intelligence, 3-540-30676-5 • Multi-Objective Machine Learning, Texte imprimé, 9783642067969 • Multi-Objective Machine Learning, Texte imprimé, 9783540818359 • Multi-objective machine learning, Yaochu Jin (ed.), Berlin, Springer, 2006, 1 vol. (xiii, 660 p.), Studies in computational intelligence, 3-540-30676-5 |
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| 245 | 0 | 0 | |a Multi-Objective Machine Learning |c edited by Yaochu Jin. |
| 250 | |a 1st ed. 2006. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 16 |x 1860-9503 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |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 | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 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 | ||
| 700 | 1 | |a Jin, Yaochu, |d 1966- |4 pbd | |
| 776 | 0 | |0 109339991 |t Multi-objective machine learning |f Yaochu Jin (ed.) |c Berlin |n Springer |d 2006 |p 1 vol. (xiii, 660 p.) |s Studies in computational intelligence |z 3-540-30676-5 | |
| 776 | 0 | |t Multi-Objective Machine Learning |b Texte imprimé |z 9783642067969 | |
| 776 | 0 | |t Multi-Objective Machine Learning |b Texte imprimé |z 9783540818359 | |
| 776 | 0 | |0 109339991 |t Multi-objective machine learning |f Yaochu Jin (ed.) |c Berlin |n Springer |d 2006 |p 1 vol. (xiii, 660 p.) |s Studies in computational intelligence |z 3-540-30676-5 | |
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