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Soft computing in machine learning

As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection...

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Detalles Bibliográficos
Autores principales: Rhee, Sang-Yong, Park, Jooyoung, Inoue, Atsushi
Lenguaje:eng
Publicado: Springer 2014
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-05533-6
http://cds.cern.ch/record/1693405
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author Rhee, Sang-Yong
Park, Jooyoung
Inoue, Atsushi
author_facet Rhee, Sang-Yong
Park, Jooyoung
Inoue, Atsushi
author_sort Rhee, Sang-Yong
collection CERN
description As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to perform some things, so that they can automatically detect, diagnose, and solve a variety of problems. The intelligent systems have made rapid progress in developing the state of the art in machine learning based on smart and deep perception. Using machine learning, the intelligent systems make widely applications in automated speech recognition, natural language processing, medical diagnosis, bioinformatics, and robot locomotion. This book aims at introducing how to treat a substantial amount of data, to teach machines and to improve decision making models. And this book specializes in the developments of advanced intelligent systems through machine learning. It consists of 11 contributions that features illumination change detection, generator of electronic educational publications, intelligent call triage system, recognition of rocks at uranium deposits, graphics processing units, mathematical model of hit phenomena, selection and mutation in genetic algorithm, hands and arms motion estimation, application of wavelet network, Kanizsa triangle illusion, and support vector machine regression. Also, it describes how to apply the machine learning for the intelligent systems. This edition is published in original, peer reviewed contributions covering from initial design to final prototypes and verifications.   
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spelling cern-16934052021-04-21T21:04:50Zdoi:10.1007/978-3-319-05533-6http://cds.cern.ch/record/1693405engRhee, Sang-YongPark, JooyoungInoue, AtsushiSoft computing in machine learningEngineeringAs users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to perform some things, so that they can automatically detect, diagnose, and solve a variety of problems. The intelligent systems have made rapid progress in developing the state of the art in machine learning based on smart and deep perception. Using machine learning, the intelligent systems make widely applications in automated speech recognition, natural language processing, medical diagnosis, bioinformatics, and robot locomotion. This book aims at introducing how to treat a substantial amount of data, to teach machines and to improve decision making models. And this book specializes in the developments of advanced intelligent systems through machine learning. It consists of 11 contributions that features illumination change detection, generator of electronic educational publications, intelligent call triage system, recognition of rocks at uranium deposits, graphics processing units, mathematical model of hit phenomena, selection and mutation in genetic algorithm, hands and arms motion estimation, application of wavelet network, Kanizsa triangle illusion, and support vector machine regression. Also, it describes how to apply the machine learning for the intelligent systems. This edition is published in original, peer reviewed contributions covering from initial design to final prototypes and verifications.   Springeroai:cds.cern.ch:16934052014
spellingShingle Engineering
Rhee, Sang-Yong
Park, Jooyoung
Inoue, Atsushi
Soft computing in machine learning
title Soft computing in machine learning
title_full Soft computing in machine learning
title_fullStr Soft computing in machine learning
title_full_unstemmed Soft computing in machine learning
title_short Soft computing in machine learning
title_sort soft computing in machine learning
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-05533-6
http://cds.cern.ch/record/1693405
work_keys_str_mv AT rheesangyong softcomputinginmachinelearning
AT parkjooyoung softcomputinginmachinelearning
AT inoueatsushi softcomputinginmachinelearning