Advances in computational intelligence and learning

Neurocomputing, 2013

Studies in computational intelligence, 2010

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Recherche, 2012

The present book includes a set of selected extended papers from the second International Joint Conference on Computational Intelligence (IJCCI 2010), held in Valencia, Spain, from 24 to 26 October 2010. The conference was composed by three co-located conferences: The International Conference on Fuzzy Computation (ICFC), the International Conference on Evolutionary Computation (ICEC), and the International Conference on Neural Computation (ICNC). Recent progresses in scientific developments and applications in ...

Lecture Notes in Computer Science, 2011

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

Neurocomputing, 2022

Recent Trends in Computational Intelligence, 2020

Machine learning has increasingly become the bridge between theoretical knowledge and practical applications, transforming countless aspects of modern life. With the development of advanced machine learning algorithms, we can now address complex real-world problems once reserved for human experts. Specifically, by leveraging vast amounts of data and powerful computing resources, machine learning algorithms can learn to recognize patterns and make predictions or decisions based on those patterns. In addition, in many cases, machine learning algorithms can outperform humans in these tasks by analyzing data more quickly and accurately than humans could. Machine learning enables new solutions to real-world problems and changes how we live, work, and interact with technology.

Pattern Recognition Letters, 2018

2006

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.