Multi-Objective Machine Learning

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Jin, Yaochu (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.
• Σειρά: Studies in Computational Intelligence, 16
• Θέματα: Engineering.; Artificial intelligence.; Statistical physics.; Dynamical systems.; Applied mathematics.; Engineering mathematics.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).; Statistical Physics, Dynamical Systems and Complexity.

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...

Πίνακας περιεχομένων:

• 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.