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Borehole imaging is among the fastest and most accurate methods for collecting high resolution subsurface data. Recent breakthroughs in acquisition, tool design, and modeling software provide real-time subsurface images of incredible detail, from the drill bit straight to a workstation. This text portrays key applications of dipmeter and image log data across the exploration and production life cycle.
This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website.
Summary Based on the experiences of past designs and the outcome of recent studies in the comparisons of low-level image processing architectures, a pipelined system for real time low-image processing has been designed and realized in CMOS technology. To minimize design pitfalls, a study was performed to the details of the design solutions that have been found in embodimentsof the three main architectural groups of image processing; the Square Processor Arrays, the Linear Processor Arrays and the Pipelines. This is reflected in a theoretical model. As the design is based on bitplane-wise processing of images, research was performed on the principles ofCellularLogic Processing of two dimensional images. of binary A methodology has been developed that is based on the transformation images using sets of Hit-or-Miss masks. This method appeared to be extendable to higher dimensional images. A theoretical model for the generation of break-point conditions in high dimensional images has been developed, and applied up to dimension three.
Neural Networks have been the theater of a dramatic increase of activities in the last five years. The interest of mixing results from fields as different as neurobiology, physics (spin glass theory), mathematics (linear algebra, statistics ... ), computer science (software engineering, hardware architectures ... ) or psychology has attracted a large number of researchers to the field. The perspective of dramatic improvements in many applications has lead important companies to launch new neural network programs and start-ups have mushroomed to address this new market. Throughout the world large programs are being set-up: in Japan the government has committed more than $18 million per year t...
This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification. Numerous examples enhance the text. Practitioners, researchers,and students in engineering and computer science will find Implementation Techniques a comprehensive and powerful reference. - Recurrent methods - Boltzmann machines - Constructive learning with methods for the reduction of complexity in neural network systems - Modular systems - Associative memory - Neural network design based on the concept of the Inductive Logic Unit - Data classification - Integrated neuron model systems that function as programmable rational approximators
This book constitutes the refereed proceedings of the sixth International Conference on Artificial Neural Networks - ICANN 96, held in Bochum, Germany in July 1996. The 145 papers included were carefully selected from numerous submissions on the basis of at least three reviews; also included are abstracts of the six invited plenary talks. All in all, the set of papers presented reflects the state of the art in the field of ANNs. Among the topics and areas covered are a broad spectrum of theoretical aspects, applications in various fields, sensory processing, cognitive science and AI, implementations, and neurobiology.
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This book constitutes the thoroughly refereed post-proceedings of the Visual Information Expert Workshop, VIEW 2006, held in Paris, France, in April 2006. The 23 revised full papers were carefully selected from numerous submissions during two rounds of reviewing and improvement. The book is categorized in three main parts: pixelization theory, pixelization applications, pixelization and cognition.
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