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    Multi-Objective Machine Learning

Multi-Objective Machine Learning

Yaochu Jin
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      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.
      Format: Hardback CONTRIBUTORS: Yaochu Jin EAN: 9783540306764 COUNTRY: Germany PAGES: WEIGHT: 2460 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG DATE PUBLISHED: 2006-02-10 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, SCIENCE / Physics / Mathematical & Computational, SCIENCE / Physics / General, TECHNOLOGY & ENGINEERING / Engineering (General) WIDTH: 155 cm SPINE:

      Book Themes:

      Cybernetics and systems theory, Mathematical physics, Maths for engineers, Artificial intelligence

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      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.
      Format: Hardback CONTRIBUTORS: Yaochu Jin EAN: 9783540306764 COUNTRY: Germany PAGES: WEIGHT: 2460 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG DATE PUBLISHED: 2006-02-10 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, SCIENCE / Physics / Mathematical & Computational, SCIENCE / Physics / General, TECHNOLOGY & ENGINEERING / Engineering (General) WIDTH: 155 cm SPINE:

      Book Themes:

      Cybernetics and systems theory, Mathematical physics, Maths for engineers, Artificial intelligence

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