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Nonlinear Modeling And Forecasting
Based on a Santa Fe Institute and NATO sponsored workshop, this book brings together the ideas of leading researchers in the rapidly expanding, interdisciplinary field of nonlinear modeling in an attempt to stimulate the cross-fertilization of ideas and the search for unifying themes. The central theme of the workshop was the construction of nonlinear models from time-series data. Approaches to this problem have drawn from the disciplines of multivariate function approximation and neural nets, dynamical systems and chaos, statistics, information theory, and control theory. Applications have been made to economics, mechanical engineering, meteorology, speech processing, biology, and fluid dynamics.
Logic, Methodology and Philosophy of Science IX
This volume is the product of the Proceedings of the 9th International Congress of Logic, Methodology and Philosophy of Science and contains the text of most of the invited lectures. Divided into 15 sections, the book covers a wide range of different issues. The reader is given the opportunity to learn about the latest thinking in relevant areas other than those in which they themselves may normally specialise.
Logic, Methodology and Philosophy of Science IX
This volume is the product of the Proceedings of the 9th International Congress of Logic, Methodology and Philosophy of Science and contains the text of most of the invited lectures. Divided into 15 sections, the book covers a wide range of different issues. The reader is given the opportunity to learn about the latest thinking in relevant areas other than those in which they themselves may normally specialise.
Digital Signal Processing with Kernel Methods
A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel ...
The Brain Abstracted
Winner of the Nayef Al-Rodhan Book Prize from The Royal Institute of Philosophy An exciting, new framework for interpreting the philosophical significance of neuroscience. All science needs to simplify, but when the object of research is something as complicated as the brain, this challenge can stretch the limits of scientific possibility. In fact, in The Brain Abstracted, an avowedly “opinionated” history of neuroscience, M. Chirimuuta argues that, due to the brain’s complexity, neuroscientific theories have only captured partial truths—and “neurophilosophy” is unlikely to be achieved. Looking at the theory and practice of neuroscience, both past and present, Chirimuuta shows ho...
Advances in Neural Information Processing Systems 9
The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The broad range of interdisciplinary research areas represented includes neural networks and genetic algorithms, cognitive science, neuroscience and biology, computer science, AI, applied mathematics, physics, and many branches of engineering. Only about 30% of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. All of the papers presented appear in these proceedings.
Neural Connectomics Challenge
This book illustrates the thrust of the scientific community to use machine learning concepts for tackling a complex problem: given time series of neuronal spontaneous activity, which is the underlying connectivity between the neurons in the network? The contributing authors also develop tools for the advancement of neuroscience through machine learning techniques, with a focus on the major open problems in neuroscience. While the techniques have been developed for a specific application, they address the more general problem of network reconstruction from observational time series, a problem of interest in a wide variety of domains, including econometrics, epidemiology, and climatology, to cite only a few. divThe book is designed for the mathematics, physics and computer science communities that carry out research in neuroscience problems. The content is also suitable for the machine learning community because it exemplifies how to approach the same problem from different perspectives./divdivbr/divdivbr
On Growth and Form
We have shown that simple power-law dynamics is expected for flexible fractal objects. Although the predicted behavior is well established for linear polymers, the situationm is considerably more complex for colloidal aggregates. In the latter case, the observed K-dependence of (r) can be explained either in terms of non-asymptotic hydrodynamics or in terms of weak power-law polydispersity. In the case of powders (alumina, in particular) apparent fractal behavior seen in static scattering is not found in the dynamics. ID. W. Schaefer, J. E. Martin, P. Wiitzius, and D. S. Cannell, Phys. Rev. Lett. 52,2371 (1984). 2 J. E. Martin and D. W. Schaefer, Phys. Rev. Lett. 5:1,2457 (1984). 3 D. W. Sch...
The Nature of Statistical Learning Theory
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Biocomputing And Emergent Computation - Proceedings Of Bcec97
This volume contains papers presented at the BCEC97 conference, held in Skövde, Sweden, in September 1997. The conference brought together researchers from biology and computer science to discuss the use of computational techniques in biology, as well as the use of biological metaphors in computing. Examples of the work presented in these papers include computer simulations of embryogenesis; algorithms for protein folding prediction; problem solving using DNA computation; neural-network learning in retina implants; and optimisation algorithms inspired by natural evolution.
