New Classification Techniques
Author: William H. Helme
Publisher:
Published: 1962
Total Pages: 24
ISBN-13:
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Advanced Classification Techniques for Healthcare Analysis
Author: Chakraborty, Chinmay
Publisher: IGI Global
Published: 2019-02-22
Total Pages: 448
ISBN-13: 1522577971
DOWNLOAD EBOOKMedical and information communication technology professionals are working to develop robust classification techniques, especially in healthcare data/image analysis, to ensure quick diagnoses and treatments to patients. Without fast and immediate access to healthcare databases and information, medical professionals’ success rates and treatment options become limited and fall to disastrous levels. Advanced Classification Techniques for Healthcare Analysis provides emerging insight into classification techniques in delivering quality, accurate, and affordable healthcare, while also discussing the impact health data has on medical treatments. Featuring coverage on a broad range of topics such as early diagnosis, brain-computer interface, metaheuristic algorithms, clustering techniques, learning schemes, and mobile telemedicine, this book is ideal for medical professionals, healthcare administrators, engineers, researchers, academicians, and technology developers seeking current research on furthering information and communication technology that improves patient care.
Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis
Author: Nilanjan Dey
Publisher: Academic Press
Published: 2019-07-31
Total Pages: 218
ISBN-13: 0128180056
DOWNLOAD EBOOKClassification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images. Examines the methodology of classification of medical images that covers the taxonomy of both supervised and unsupervised models, algorithms, applications and challenges Discusses recent advances in Artificial Neural Networks, machine learning, and deep learning in clinical applications Introduces several techniques for medical image processing and analysis for CAD systems design
Research to Improve Enlisted Classification Techniques
Author: William H. Helme
Publisher:
Published: 1964
Total Pages: 36
ISBN-13:
DOWNLOAD EBOOKResearch responsive to the Army requirement for maintenance and continued development of the aptitude area system of differential classification of enlisted men is reviewed. Research effort of the NEW CLASSIFICATION TECHNIQUES Task has been devoted substantially to improved measures for the Army Classification Battery (ACB) and identification of combinations of tests which are the most effective differential predictors of success in occupational areas and subareas. Additional Task objectives encompass (1) identifying potential career enlisted men; (2) screening and assignment of enlisted men of relatively low ability, (3) developing physical proficiency measures to classify EM for combat and combat-support MOS with unusual physical demands. New Classification tests developed and ready for comprehensive evaluation as potential components of the ACB include: aptitude and ability tests for Electronics, General Maintenance, Motor Maintenance, and Clerical job areas; three information tests for Construction and Mechanical-Electrical jobs; and personality-interest measures.
Machine Learning Models and Algorithms for Big Data Classification
Author: Shan Suthaharan
Publisher: Springer
Published: 2015-10-20
Total Pages: 364
ISBN-13: 1489976418
DOWNLOAD EBOOKThis book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
Supervised Machine Learning for Text Analysis in R
Author: Emil Hvitfeldt
Publisher: CRC Press
Published: 2021-10-22
Total Pages: 402
ISBN-13: 1000461971
DOWNLOAD EBOOKText data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
Data Classification
Author: Charu C. Aggarwal
Publisher: CRC Press
Published: 2014-07-25
Total Pages: 710
ISBN-13: 1498760589
DOWNLOAD EBOOKComprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlyi
Mining Text Data
Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
Published: 2012-02-03
Total Pages: 527
ISBN-13: 1461432235
DOWNLOAD EBOOKText mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.
Cluster and Classification Techniques for the Biosciences
Author: Alan H. Fielding
Publisher: Cambridge University Press
Published: 2006-12-14
Total Pages: 4
ISBN-13: 1139460064
DOWNLOAD EBOOKAdvances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique's potential.
Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning
Author: Rani, Geeta
Publisher: IGI Global
Published: 2020-10-16
Total Pages: 586
ISBN-13: 1799827437
DOWNLOAD EBOOKBy applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.
