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Unsupervised Feature Extraction Applied to Bioinformatics: A Pca Based and TD Based Approach

This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that;

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel;

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Computational Methods With Applications in Bioinformatics Analysis

types of computational techniques to tackle different biomedical applications. These techniques include unsupervised learning algorithms;

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Supervised and Unsupervised Learning for Data Science

in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded;

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Unsupervised Learning

A new approach to unsupervised learning Evolving technologies have brought about an explosion of information in recent years, but the;

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Supervised and Unsupervised Pattern Recognition

research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy;

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Object-Based Image Analysis and Treaty Verification

of nuclear non-proliferation. A comprehensive workflow and newly developed algorithms for object-based high resolution image (pre-) processing;

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A Beginner's Guide to Image Shape Feature Extraction Techniques

of content based image retrieval Includes polygonal approximation techniques of shape features Details moment based, scale space and;

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Feature Extraction Approaches for Optical Character Recognition

step in order to properly operate. This book is a large-scale review of the feature extraction approaches for character recognition based on;

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Image Registration

This book presents a thorough and detailed guide to image registration, outlining the principles and reviewing state-of-the-art tools and;

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A Beginner's Guide to Image Shape Feature Extraction Techniques

. Focussing on a shape feature extraction technique used in content-based image retrieval (CBIR), it explains different applications of image shape;

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Data Mining for Bioinformatics

mining has to offer Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data;

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Data Mining for Bioinformatics

mining has to offer Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data;

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Principal Component Analysis Networks and Algorithms

This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA;

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Perspectives on Pattern Recognition

Pattern recognition involves tasks such as pattern identification, feature extraction from them and pattern classification in categories;

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Spectral Feature Selection for Data Mining

algorithms, and applications. A timely introduction to spectral feature selection, this book illustrates the potential of this powerful;

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Spectral Feature Selection for Data Mining

algorithms, and applications. A timely introduction to spectral feature selection, this book illustrates the potential of this powerful;

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Data Mining in Biomedical Imaging, Signaling, and Systems

unsupervised learning methods. Both feature extraction and supervised learning are discussed as they apply to seizure-related patterns in epilepsy;

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Data Mining in Biomedical Imaging, Signaling, and Systems

unsupervised learning methods. Both feature extraction and supervised learning are discussed as they apply to seizure-related patterns in epilepsy;

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New Developments in Unsupervised Outlier Detection

This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k;

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Deep Learning for Chest Radiographs

footprints and kills more children as compared to any other immunity-based disease, causing up to 15% of child deaths per year, especially;

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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniq

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas;

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Computational Methods of Feature Selection

in bioinformatics, including feature construction as well as redundancy-, ensemble-, and penalty-based feature selection. Through a clear, concise, and;

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Algorithms in Machine Learning Paradigms

, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence;

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Algorithms in Machine Learning Paradigms

, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence;

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Unsupervised Learning Algorithms

detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field.;

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Texture Feature Extraction Techniques for Image Recognition

also covers applications related to texture features, such as facial imaging. It is a valuable resource for machine vision researchers and;

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