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Soft Computing in Machine Learning

  • Sang-Yong Rhee
  • Jooyoung Park
  • Atsushi Inoue

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 273)

Table of contents

  1. Front Matter
    Pages 1-9
  2. Bayanmunkh Odgerel, Chang Hoon Lee
    Pages 1-10
  3. Altynbek Sharipbay, Assel Omarbekova, Alma Zakirova
    Pages 11-20
  4. E. Amirgaliev, Z. Isabaev, S. Iskakov, Y. Kuchin, R. Muhamediyev, E. Muhamedyeva et al.
    Pages 33-40
  5. Yasuko Kawahata, Etsuo Genda, Chinami Hara, Akira Ishii
    Pages 53-59
  6. Noritaka Shigei, Kentaro Araki, Hiromi Miyajima
    Pages 61-73
  7. Takashi Samatsu, Yoshito Sonoda
    Pages 85-93
  8. Tomoko Sakiyama, Aisato Sasaki, Yukio-Pegio Gunji
    Pages 95-103
  9. Chen-Chia Chuang, Jin-Tsong Jeng, Guan-Yi Hu
    Pages 105-115
  10. Back Matter
    Pages 117-117

About these proceedings

Introduction

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. 

 

Keywords

ISIS2013 Intelligent Systems Machine Learning Soft Computing Symposium on Advanced Intelligent Systems

Editors and affiliations

  • Sang-Yong Rhee
    • 1
  • Jooyoung Park
    • 2
  • Atsushi Inoue
    • 3
  1. 1.Kyungnam UniversityGyeongnamKorea, Republic of (South Korea)
  2. 2.Korea UniversitySeojongKorea, Republic of (South Korea)
  3. 3.Eastern Washington UniversityWashingtonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-05533-6
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-05532-9
  • Online ISBN 978-3-319-05533-6
  • Series Print ISSN 2194-5357
  • Series Online ISSN 2194-5365
  • Buy this book on publisher's site
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