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Feature Extraction Foundations and Applications (Studies in Fuzziness and Soft Computing)

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Published by Springer .
Written in English


  • Computer vision,
  • Computer Graphics - General,
  • Applied,
  • Computers,
  • Mathematics,
  • Computer Books: General,
  • Computer Science,
  • Feature Extraction,
  • Feature Selection,
  • Machine Learning,
  • Mathematics / Applied,
  • Statistical Learning,
  • Artificial Intelligence - General

Book details:

Edition Notes

ContributionsIsabelle Guyon (Editor), Steve Gunn (Editor), Masoud Nikravesh (Editor), Lotfi A. Zadeh (Editor)
The Physical Object
Number of Pages778
ID Numbers
Open LibraryOL12774222M
ISBN 103540354875
ISBN 109783540354871

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a unified view of the feature extraction problem. Section 2 is an overview of the methods and results presented in the book, emphasizing novel contribu-tions. Section 3 provides the reader with an entry point in the field of feature extraction by showing small revealing examples and describing simple but ef-fective algorithms. Author: Mark Nixon; Publisher: Elsevier ISBN: Category: Computers Page: View: DOWNLOAD NOW» Whilst other books cover a broad range of topics, Feature Extraction and Image Processing takes one of the prime targets of applied computer vision, feature extraction, and uses it to provide an essential guide to the implementation of image . "Feature extraction finds application in biotechnology, industrial inspection, the Internet, radar, sonar, and speech recognition. This book will make a difference to the literature on machine learning." Simon Haykin, Mc Master University "This book sets a high standard as the public record of an interesting and effective competition."Brand: Springer-Verlag Berlin Heidelberg. This chapter introduces the reader to the various aspects of feature extraction covered in this book. Section 1 reviews definitions and notations and proposes a unified view of the feature extraction problem. Section 2 is an overview of the methods and results presented in the book, emphasizing novel by:

-The book owes it origin to a competition, followed by a Neural Information Processing Systems (NIPS) Workshop that was held in December - Most, important, the book embodies many of the-state-of-the-art methods in feature extraction. Simply put, the book will make a difference to the literature on machine learning. Feature extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy. Many machine learning practitioners believe that properly optimized feature extraction is the key to effective model construction. Feature extraction is the procedure of selecting a set of F features from a data set of N features, F. Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated.

There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles. Feature extraction Dimensionality reduction includes a set of techniques to help deal with the problem of the curse of dimensionality. These techniques are aimed at reducing the number of variables to be considered by the models we build, generally falling into feature selection and feature extraction. Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Bag of Words feature extraction. Text feature extraction is the process of transforming what is essentially a list of words into a feature set that is usable by a classifier. The NLTK classifiers expect dict style feature sets, so we must therefore transform our text into a dict.