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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2006.
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| Edición: | 1st ed. 2006. |
| Colección: | Studies in Computational Intelligence,
16 |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 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.