Intelligent Data Engineering And Automated Learning Ideal 2009

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Intelligent Data Engineering and Automated Learning - IDEAL 2009

Author: Emilio Corchado
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-09-07
This book constitutes the refereed proceedings of the 10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009, held in Burgos, Sapin, in September 2009. The 100 revised full papers presented were carefully reviewed and selected from over 200 submissions for inclusion in the book. The papers are organized in topical sections on learning and information processing; data mining and information management; neuro-informatics, bio-informatics, and bio-inspired models; agents and hybrid systems; soft computing techniques in data mining; recent advances on swarm-based computing; intelligent computational techniques in medical image processing; advances on ensemble learning and information fursion; financial and business engineering (modeling and applications); MIR day 2009 - Burgos; and nature inspired models for industrial applications.
Intelligent Data Engineering and Automated Learning -- IDEAL 2010

Author: Colin Fyfe
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-08-19
This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010, held in Paisley, Scotland, in September 2010. The 47 revised full papers presented were carefully reviewed and selected from many submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence.
Intelligent Data Engineering and Automated Learning – IDEAL 2018

This two-volume set LNCS 11314 and 11315 constitutes the thoroughly refereed conference proceedings of the 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, held in Madrid, Spain, in November 2018. The 125 full papers presented were carefully reviewed and selected from 204 submissions. These papers provided a timely sample of the latest advances in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, deep learning neural networks, probabilistic modelling, particle swarm intelligence, big data analytics, and applications in image recognition, regression, classification, clustering, medical and biological modelling and prediction, text processing and social media analysis.