rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Multi-Objective Machine Learning
  name:
    name part: Jin, Yaochu.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2006
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 660 p. 254 illus. online resource.
  abstract: 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.
  table of contents: 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 fo…
  note: edited by Yaochu Jin.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Statistical physics
  subject:
    topic: Dynamical systems
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Engineering
  subject:
    topic: Appl.Mathematics/Computational Methods of Engineering
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Statistical Physics, Dynamical Systems and Complexity
  classification: TA329-348
  classification: TA640-643
  classification: 519
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Computational Intelligence, 16
  identifier: 9783540330196
  identifier: http://dx.doi.org/10.1007/3-540-33019-4
  record info:
    record creation date: 100301
    record change date: 20151204160918.0
    record identifier: 978-3-540-33019-6