TAILIEUCHUNG - MEDICAL INFORMATICS Knowledge Management and Data Mining in Biomedicine

The field of medical informatics has grown rapidly over the past decade due to the advances in biomedical computing, the abundance of biomedical and genomic data, the ubiquity of the Internet, and the general acceptance of computing in various aspects of medical, biological, and health care research and practice. This book aims to be complementary to several other popular introductory medical informatics textbooks. The focus of this book is on the new concepts, technologies, and practices of biomedical knowledge management, data mining, and text mining that are beginning to bring useful "knowledge" to biomedical professionals and researchers. The book will serve as a textbook or reference book for medical. | Springer s Integrated Series in Information Systems MEDICAL INFORMATICS Volume Contributors Daniel Berieanl Olivier Bodenreider Anita Burgun Michael Chau Christopher G. Chute Jeff Collmann Ted Cooper Julie Dickerson Jing Ding Pan Du Shauna Eggers Millicent Eidson Marcelo Fiszman Carol Foster Ivan Gotham Zan Huang Catherine A Larson Gondy Leroy Ling Li Bisharah Libbus Chienting Lin Cecil Lynch Mark Mime Snehasis Mukhopadhyay Daniel McDonald Matthew Palakal Asraa Rashid Debra Revere Thomas Rmdflesch Larry Smith Matthew Stephens Hua Su Lorraine Tanabe Peter Tarczy-Homoch George R. Thoma Chun-Ju Tseng w. John Wilbur Peter Winkelstein Eve Syrkin Wurtele Jennifer J. Xu Lijun Yan Terry s. Yoo Daniel Zeng Knowledge Management and Data Mining in Biomedicine William Hersh edited by Hsinchun Chen Sherrilynne s. Fuller Carol Friedman MEDICAL INFORMATICS Knowledge Management and Data Mining in Biomedicine INTEGRATED SERIES IN INFORMATION SYSTEMS Series Editors Professor Ramesh Sharda Oklahoma State University Prof. Dr. Stefan VoB Universităt Hamburg Other published titles in the series E-BUSINESS MANAGEMENT integration of Web Technologies with Business Models Michael J. Shaw VIRTUAL CORPORATE UNIVERSITIES A Matrix of Knowledge and Learning for the New Digital Dawn Walter . Baets Gert Van der Linden SCALABLE ENTERPRISE SYSTEMS An Introduction to Recent Advances edited by Vittal Prabhu Soundar Kumara Manjunath Kamath LEGAL PROGRAMMING Legal Compliance for RFID and Software Agent Ecosystems in Retail Processes and Beyond Brian Subirana and Malcolm Bain LOGICAL DATA MODELING What It Is and How To Do It Alan Chmura and J. Mark Heumann DESIGNING AND EVALUATING E-MANAGEMENT DECISION TOOLS The Integration of Decision and Negotiation Models into Internet-Multimedia Technologies Giampiero . Beroggi INFORMATION AND MANAGEMENT SYSTEMS FOR PRODUCT CUSTOMIZATION Blecker Friedrich Kaluza Abdelkafi .

• Y học thường thức
• Machine Learning
• Legal Issues
• Mapping Medical
• Informatics Research
• Bioinformatics Challenges
• Biological Perspective
• Lecture Machine learning
• Machine learning algorithms
• Data analysis methods
• Max margin learning
• Multi instance learning
• Reinforcement learning
• Delayed rewards
• Machine Learning Introduction
• Machine Learning Work
• Types of Machine Learning
• Supervised Learning
• Forms of Supervised Learning
• Bayesian Learning
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• Boltzmann machines
• Linear prediction
• Linear regression models
• Maximum likelihood
• Gaussian distributions
• Nonlinear ridge regression risk
• Cross validation
• Newton’s algorithm
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• Logistic regression
• Logistic regression likelihood
• Formulate layers
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• Convolutional neural networks
• Design convolutional layers
• Recurrent nets
• Recurrent neural networks
• Lecture Introduction to Machine learning and Data mining
• Bài giảng Học máy và khai phá dữ liệu
• Machine learning and data mining
• Linear regression
• Learning a regression function
• Unsupervised learning
• Partition based clustering
• The sewing machine classroom
• Learning the ins
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• Mastering the materials
• Demystifying your machine
• Increasing your sphere
• Deep Belief Network
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• Support Vector Machine
• Automatic CNC program
• Challenges in Machine Learning
• Data Mining
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• Beyond classification and regression
• Distributed data mining
• Machine learning bottleneck
• Introduction to machine learning
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• Correntropy induced loss
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• Support vector machines
• Separating hyperplane
• Artificial neural network
• Rectified linear unit
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• Phenotype prediction
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