Multi-Objective Machine Learning

Name: Multi-Objective Machine Learning
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 9783540306764
Price: 1152.16 RON
Availability: InStock

Autor: Yaochu Jin

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Legare: Copertă tare

Editura: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

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Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine...

Informații despre carte

Autor

Yaochu Jin

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2006

Pagini

660

EAN

9783540306764

ISBN

3540306765

Enbook ID

01561468

Editura

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Greutate

2460

Dimensiuni

155 x 235 x 43

Descriere completă

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. TOC:Part I. Multi-objective Feature Selection.- Part II. Multi-objective Model Selection for Better Accuracy.- Part III. Multi-objective Learning for Better Interpretability.- Part IV. Generation of Ensembles using Multi-objective Optimization.- Part V. Applications of Multi-objective Machine Learning.

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Multi-Objective Machine Learning

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