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A comprehensive and comparative examination of the concept of recognition across history and disciplines.
Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way. A Deterministic View of Learning in Dynamic Environments The authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems. A New Model of Information Processing This book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).
One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.
The Oxford ILDC online database, an online collection of domestic court decisions which apply international law, has been providing scholars with insights for many years. This ILDC Casebook is the perfect companion, introducing key court decisions with brief introductory and connecting texts. An ideal text for practitioners, judged, government officials, as well as for students on international law courses, the ILDC Casebook explains the theories and doctrines underlying the use by domestic courts of international law, and illustrates the key importance of domestic courts in the development of international law.
A powerful tool for anyone in employment, no matter what their field, The Recognition Book examines the traits, behaviors, and skills fundamental to doing an excellent job and demonstrates how to shine in today's competitive corporate world. Packed with case studies, practical tools, techniques, hints, and tips, the book is a useful reference guide for all. This unique and engaging book is essential reading for anyone wanting to stand out from the crowd and become the model employee. Whether you work for a multi-national or small business, whether you have just started your career or you're a seasoned executive, this guide will help you develop and hone all the attributes that make you invaluable to your employer.
Knowledge-Based Intelligent Techniques in Character Recognition presents research results on intelligent character recognition techniques, reflecting the tremendous worldwide interest in the applications of knowledge-based techniques in this challenging field. This resource will interest anyone involved in computer science, computer engineering, applied mathematics, or related fields. It will also be of use to researchers, application engineers and students who wish to develop successful character recognition systems such as those used in reading addresses in a postal routing system or processing bank checks. Features
This book deals with the relevance of recognition and validation of non-formal and informal learning education and training, the workplace and society. In an increasing number of countries, it is at the top of the policy and research agenda ranking among the possible ways to redress the glaring lack of relevant academic and vocational qualifications and to promote the development of competences and certification procedures which recognise different types of learning, including formal, non-formal and informal learning. The aim of the book is therefore to present and share experience, expertise and lessons in such a way that enables its effective and immediate use across the full spectrum of country contexts, whether in the developing or developed world. It examines the importance of meeting institutional and political requirements that give genuine value to the recognition of non-formal and informal learning; it shows why recognition is important and clarifies its usefulness and the role it serves in education, working life and voluntary work; it emphasises the importance of the coordination, interests, motivations, trust and acceptance by all stakeholders. The volume is also premised on an understanding of a learning society, in which all social and cultural groups, irrespective of gender, race, social class, ethnicity, mental health difficulties are entitled to quality learning throughout their lives. Overall the thrust is to see the importance of recognising non-formal and informal learning as part of the larger movement for re-directing education and training for change. This change is one that builds on an equitable society and economy and on sustainable development principles and values such as respect for others, respect for difference and diversity, exploration and dialogue.
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.
The concept of "funds of knowledge" is based on a simple premise: people are competent and have knowledge, and their life experiences have given them that knowledge. The claim in this book is that first-hand research experiences with families allow one to document this competence and knowledge, and that such engagement provides many possibilities for positive pedagogical actions. Drawing from both Vygotskian and neo-sociocultural perspectives in designing a methodology that views the everyday practices of language and action as constructing knowledge, the funds of knowledge approach facilitates a systematic and powerful way to represent communities in terms of the resources they possess and how to harness them for classroom teaching. This book accomplishes three objectives: It gives readers the basic methodology and techniques followed in the contributors' funds of knowledge research; it extends the boundaries of what these researchers have done; and it explores the applications to classroom practice that can result from teachers knowing the communities in which they work. In a time when national educational discourses focus on system reform and wholesale replicability across school sites, this book offers a counter-perspective stating that instruction must be linked to students' lives, and that details of effective pedagogy should be linked to local histories and community contexts. This approach should not be confused with parent participation programs, although that is often a fortuitous consequence of the work described. It is also not an attempt to teach parents "how to do school" although that could certainly be an outcome if the parents so desired. Instead, the funds of knowledge approach attempts to accomplish something that may be even more challenging: to alter the perceptions of working-class or poor communities by viewing their households primarily in terms of their strengths and resources, their defining pedagogical characteristics. Funds of Knowledge: Theorizing Practices in Households, Communities, and Classrooms is a critically important volume for all teachers and teachers-to-be, and for researchers and graduate students of language, culture, and education.
The process of learning words and languages may seem like an instinctual trait, inherent to nearly all humans from a young age. However, a vast range of complex research and information exists in detailing the complexities of the process of word learning. Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence strives to combine cross-disciplinary research into one comprehensive volume to help readers gain a fuller understanding of the developmental processes and influences that makeup the progression of word learning. Blending together developmental psychology and artificial intelligence, this publication is intended for researchers, practitioners, and educators who are interested in language learning and its development as well as computational models formed from these specific areas of research.