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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Multi-Objective Machine Learning

Multi-Objective Machine Learning

Jin, Yaochu

Paperback

Series: Studies in Computational Intelligence, Book 16

General ComputersGeneral MathematicsPhysics

ISBN10: 3642067964
ISBN13: 9783642067969
Publisher: Springer Nature
Published: Nov 22 2010
Pages: 660
Weight: 2.05
Height: 1.36 Width: 6.14 Depth: 9.21
Language: English

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.

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