Soft Computing in Machine Learning

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Rhee, Sang-Yong (Επιμελητής έκδοσης), Park, Jooyoung (Επιμελητής έκδοσης), Inoue, Atsushi (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2014.
• Σειρά: Advances in Intelligent Systems and Computing, 273
• Θέματα: Engineering.; Artificial intelligence.; Computational intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).

 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.   . 

 Sudden Illumination Change Detection and Image Contrast Enhancement -- Frame concept for generator of electronic educational publications -- Intelligent call triage system with algorithm combining decision-tree and SVM -- Recognition of Rocks at Uranium Deposits by using a Few Methods of Machine Learning -- More Faster Self-Organizing Maps by General Purpose on Graphics Processing Units -- Analysis of Local Concerts using Facebook Adapting the Mathematical Model of Hit Phenomena -- Hands and arms motion estimation of a car driver with depth image sensor by using particle filter -- SNR Improvement of an Optical Wave Microphone Using a Wavelet Network -- Origin of Kanizsa triangle illusion -- LTS-SVMR for Modeling of Nonlinear Systems with Noise and Outliers.