Imbalanced Learning

Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduc­tion to learning and a variety of policies for learning.

Correlative Learning: A Basis for Brain and Adaptive Systems provides a bridge between three disciplines: computational neuroscience, neural networks, and signal processing. First, the authors lay down the preliminary neuroscience background for engineers. The book also presents an overview of the role of correlation in the human brain as well as in the adaptive signal processing world; unifies many well-established synaptic adaptations (learning) rules within the correlation-based learning framework, focusing on a particular correlative learning paradigm, ALOPEX; and presents case studies that illustrate how to use different computational tools and ALOPEX to help readers understand certain brain functions or fit specific engineering applications.

Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following: Discovering activity patterns that emerge from behavior-based sensor data Recognizing occurrences of predefined or discovered activities in real time Predicting the occurrences of activities The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use. With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.

This is an updated version of a comprehensive a resource for health professionals working with people (primarily adults) who have learning disabilities (intellectual disabilities). It will provide a comprehensive overview of the diverse approaches to treatment and management issues, as well as methods to help them achieve the rehabilitation aims – including the usage of non-healthcare facilities. Split into three sections, the book covers 1: Underlying theory; 2: Assessment; 3: Practical methods of physical treatment and management. Combines history and the theory underlying learning disability and its associated conditions; assessment, and practical physical treatment. Achieves carefully assessed aims through methods appropriate to the individual client. Funding suggestions for working in non-healthcare situations. Features a breadth of content that also appeals to non-physiotherapists.

A new approach to unsupervised learning Evolving technologies have brought about an explosion of information in recent years, but the question of how such information might be effectively harvested, archived, and analyzed remains a monumental challenge—for the processing of such information is often fraught with the need for conceptual interpretation: a relatively simple task for humans, yet an arduous one for computers. Inspired by the relative success of existing popular research on self-organizing neural networks for data clustering and feature extraction, Unsupervised Learning: A Dynamic Approach presents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM) and the more advanced self-organizing hierarchical variance map (SOHVM). It covers a series of pertinent, real-world applications with regard to the processing of multimedia data—from its role in generic image processing techniques, such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data. Self-organization concepts and applications discussed include: Distance metrics for unsupervised clustering Synaptic self-amplification and competition Image retrieval Impulse noise removal Microbiological image analysis Unsupervised Learning: A Dynamic Approach introduces a new family of unsupervised algorithms that have a basis in self-organization, making it an invaluable resource for researchers, engineers, and scientists who want to create systems that effectively model oppressive volumes of data with little or no user intervention.

Learning disability nurses play a leading role in the care and support of people with learning disabilities. Learning Disability Nursing explores the theory and practice of learning disability nursing, with an emphasis on understanding the experiences of learning disability nurses as they seek to practice effectively. It examines key issues faced by people with learning disabilities and those who support them and promotes the role of knowledgeable and reflective practitioners. * Develops a framework for learning disability nursing practice * Examines key issues for people with learning disabilities * Identifies distinctive features of learning disability nursing * Supports learning disability nurses in developing their roles and to support those with learning disabilities in exercising their rights. * Explores ethical and moral dilemmas, challenges and successes

In this updated landmark book, the authors have gathered the seminal work and most current thinking on adult learning into one volume. Learning in Adulthood addresses a wide range of topics including: Who are adult learners? How do adults learn? Why are adults involved in learning activities? How does the social context shape the learning that adults are engaged in? How does aging affect learning ability?

As more than 90% of spending on the Internet comes from brick and mortar companies it is these operations that will form the client base for e-learning. This book shows those companies how to get e-learning implementation right first time. Don Morisson explores and explains the whole implementation continuum – strategy, vendor selection, technology, implementation, culture change, content development and delivery. Most importantly he stresses that the success or failure of an e-learning initiative is directly related to the underlying strategic thinking. Written for a more mature, second generation e-learning market the book provides a practitioner's handbook to both guide the novice and inform the veteran. * Focuses on the reader's needs * Focuses on the strategic issues of e-learning * Informed by key business drivers * Supported and endorsed by PWC Readership: Senior managers including CEOs, CIOs, CLOs, HR Directors, middle management responsible for implementing and/or delivering e-learning, consultants

The new edition of the authoritative book in the field of adult education – fully revised to reflect the latest research and practice implications. For nearly three decades, Learning in Adulthood has been the definitive guide in the field of adult education. Now in its fourth edition, this comprehensive volume is fully revised to reflect the latest developments in theory, research, and practice. The authors integrate foundational research and current knowledge to present fresh, original perspectives on teaching and learning in adulthood. Written by internationally-recognized experts, this market-leading guide draws from work in sociology, philosophy, critical social theory, psychology, and education to provide an inclusive overview of adult learning. Designed primarily for educators of adults, this book is accessible for readers new to adult education, yet suitably rigorous for those more familiar with the subject. Content is organized into four practical parts, covering topics such as the social context of adult learning, self-directed and transformational learning, postmodern and feminist perspectives, cognitive development in adulthood, and more. Offering the most comprehensive single-volume treatment of adult learning available, this landmark text: Offers a wide-ranging perspective on adult learning Synthesizes the latest thinking and work in the field Includes coverage of the sociocultural perspectives of adult learning Explores the broader social implications of adult education Learning in Adulthood: A Comprehensive Guide, 4th Edition is an indispensable resource for educators and administrators involved in teaching adults, as well as faculty and students in graduate programs in adult education.

An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.