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Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence (Industrial Electronics)
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Hybrid Tow Feature Extraction Descriptor For Shape Pattern Recognition
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A Beginner’s Guide to Image Shape Feature Extraction Techniques (Intelligent Signal Processing and Data Analysis)
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Feature Extraction and Image Processing for Computer Vision
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Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Superv…
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Feature Extraction and Image Processing for Computer Vision
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Encyclopedia of Biometrics
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Computational Intelligence in Multi-Feature Visual Pattern Recognition: Hand Posture and Face Recognition using Biological…
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Pattern Recognition and Classification: An Introduction
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Principal Component Analysis Networks and Algorithms
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Image Registration: Principles, Tools and Methods (Advances in Computer Vision and Pattern Recognition)
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Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition
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Machine and Deep Learning
Artificial neural networks are computational systems vaguely inspired by design of natural neural networks (NNN). These systems are also called connectionist systems. Learn what is Artificial Neural Networks in detail through this blog.
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The Independent Component Analysis (ICA) plays very important role in blind source separation and has many more applications in pattern recognition. The ICA is new area for researchers in the last decade for face recognition. There is much more scope for research using ICA for face recognition with different methods of feature extractions and needs to be addressed. As the promising applications of ICA is feature extraction, where it extracts independent image bases which are not necessarily orthogonal and it is sensitive to high order statistics. In the task of face recognition, important information may be contained in the high order relationship among pixels. Independent Component Analysis (ICA) minimizes both second order and higher-order dependencies in the input data and attempts to find the basis along with the data when projected onto them are statistically independent. So ICA seems to be a promising face feature extraction method. Face Recognition Using Independent Component Analysis (Paperback)
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Cyber space is now an essential part of our societies and economies, and may even become a determining factor in our lives. Even though most of business and projects, especially those in transnational environment, are highly dependent on information technology, many of them underestimate the risks of cyberspace and suffer business costs caused by hacker attacks, viruses and malware. Cybercrime is already flourishing in areas with weak cyber governance like in Eastern Europe, and consequently in Romania. International, EU regional and national vigilance and collaborative intelligence are crucial. A global dialogue among governments, society, and the private sector, can help create agreement on how to secure the Internet economy. Romania government should also cooperate in developing an international and regional information exchange and early warning mechanisms in cooperation with strategic partners, USA and NATO, for rapid reaction in case of crisis, as well as establishing EU Cyberspace Policy Framework. Early Warning mechanism in cyber space (Paperback)
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Fundamentals of pattern recognition.- Feature extraction and classification.- Computer vision.- Image processing and analysis.- Video processing and analysis.- Biometric and action recognition.- Biomedical image analysis.- Document and speech analysis.- Pattern recognition applications. • ISBN:9783662456453 • Format:Paperback • Publication Date:2014-11-20
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Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow obje
ct class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition. Pattern Recognition: Recent Advances (Hardcover)
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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 contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today’s advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem. • ISBN:9781461376224 • Format:Paperback • Publication Date:2012-10-11
