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Foundational Issues in Human Brain Mapping
The field of neuroimaging has reached a watershed and critiques and emerging trends are raising foundational issues of methodology, measurement, and theory. Here, scholars reexamine these issues and explore controversies that have arisen in cognitive science, cognitive neuroscience, computer science, and signal processing.
Computational Learning Theory and Natural Learning Systems: Intersections between theory and experiment
Annotation These original contributions converge on an exciting and fruitful intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning. Bridging theory and practice, computer science and psychology, they consider general issues in learning systems that could provide constraints for theory and at the same time interpret theoretical results in the context of experiments with actual learning systems. In all, nineteen chapters address questions such as, What is a natural system? How should learning systems gain from prior knowledge? If prior knowledge is important, how can we quantify how important? What ...
Computational Learning Theory and Natural Learning Systems: Making learning systems practical
Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.
Connectionist Modeling and Brain Function
Bringing together contributions in biology, neuroscience, computer science, physics, and psychology, this book offers a solid tutorial on current research activity in connectionist-inspired biology-based modeling. It describes specific experimental approaches and also confronts general issues related to learning associative memory, and sensorimotor development. Introductory chapters by editors Hanson and Olson, along with Terrence Sejnowski, Christof Koch, and Patricia S. Churchland, provide an overview of computational neuroscience, establish the distinction between "realistic" brain models and "simplified" brain models, provide specific examples of each, and explain why each approach might...
Computational Learning Theory and Natural Learning Systems: Selecting good models
Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.
Machine Learning: From Theory to Applications
This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.
Computer Vision -- ECCV 2014
The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.
Handbook of Categorization in Cognitive Science
Categorization, the basic cognitive process of arranging objects into categories, is a fundamental process in human and machine intelligence and is central to investigations and research in cognitive science. Until now, categorization has been approached from singular disciplinary perspectives with little overlap or communication between the disciplines involved (Linguistics, Psychology, Philosophy, Neuroscience, Computer Science, Cognitive Anthropology). Henri Cohen and Claire Lefebvre have gathered together a stellar collection of contributors in this unique, ambitious attempt to bring together converging disciplinary and conceptual perspectives on this topic. "Categorization is a key conc...
Computational and Network Modeling of Neuroimaging Data
Neuroimaging is witnessing a massive increase in the quality and quantity of data being acquired. It is widely recognized that effective interpretation and extraction of information from such data requires quantitative modeling. However, modeling comes in many diverse forms, with different research communities tackling different brain systems, different spatial and temporal scales, and different aspects of brain structure and function. Computational and Network Modeling of Neuroimaging Data provides an authoritative and comprehensive overview of the many diverse modeling approaches that have been fruitfully applied to neuroimaging data. This book gives an accessible foundation to the field o...
