Soft Computing in Machine Learning

Rhee, Sang-Yong|Park, Jooyoung|Inoue, Atsushi

ISBN 10: 3319055321 / ISBN 13: 9783319055329

Published by Springer International Publishing, 2014

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in soft computing in machine learningScientific outcome of the 14th International Symposium on Advanced Intelligent Systems (ISIS2013), held on November 13 - 16, 2013 at PaiChai University in Daejeon, Korea focusing on Soft Computi. Seller Inventory # 4497304

Bibliographic Details

Title: Soft Computing in Machine Learning

Publisher: Springer International Publishing

Publication Date: 2014

Binding: Kartoniert / Broschiert

Condition: New

About this title

Synopsis:

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.