Download or read book On the Complexity of Pattern Recognition Algorithms on a Tree structured Parallel Computer written by University of Minnesota. Institute for Mathematics and Its Applications and published by . This book was released on 1989 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Parallel Processing for Artificial Intelligence 1 written by L.N. Kanal and published by Elsevier. This book was released on 2014-06-28 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parallel processing for AI problems is of great current interest because of its potential for alleviating the computational demands of AI procedures. The articles in this book consider parallel processing for problems in several areas of artificial intelligence: image processing, knowledge representation in semantic networks, production rules, mechanization of logic, constraint satisfaction, parsing of natural language, data filtering and data mining. The publication is divided into six sections. The first addresses parallel computing for processing and understanding images. The second discusses parallel processing for semantic networks, which are widely used means for representing knowledge - methods which enable efficient and flexible processing of semantic networks are expected to have high utility for building large-scale knowledge-based systems. The third section explores the automatic parallel execution of production systems, which are used extensively in building rule-based expert systems - systems containing large numbers of rules are slow to execute and can significantly benefit from automatic parallel execution. The exploitation of parallelism for the mechanization of logic is dealt with in the fourth section. While sequential control aspects pose problems for the parallelization of production systems, logic has a purely declarative interpretation which does not demand a particular evaluation strategy. In this area, therefore, very large search spaces provide significant potential for parallelism. In particular, this is true for automated theorem proving. The fifth section considers the problem of constraint satisfaction, which is a useful abstraction of a number of important problems in AI and other fields of computer science. It also discusses the technique of consistent labeling as a preprocessing step in the constraint satisfaction problem. Section VI consists of two articles, each on a different, important topic. The first discusses parallel formulation for the Tree Adjoining Grammar (TAG), which is a powerful formalism for describing natural languages. The second examines the suitability of a parallel programming paradigm called Linda, for solving problems in artificial intelligence.Each of the areas discussed in the book holds many open problems, but it is believed that parallel processing will form a key ingredient in achieving at least partial solutions. It is hoped that the contributions, sourced from experts around the world, will inspire readers to take on these challenging areas of inquiry.
Download or read book Pattern Recognition Algorithms for Data Mining written by Sankar K. Pal and published by CRC Press. This book was released on 2004-05-27 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, me
Download or read book Parallel Processing for Artificial Intelligence 3 written by J. Geller and published by Elsevier. This book was released on 1997-02-10 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third in an informal series of books about parallel processing for Artificial Intelligence, this volume is based on the assumption that the computational demands of many AI tasks can be better served by parallel architectures than by the currently popular workstations. However, no assumption is made about the kind of parallelism to be used. Transputers, Connection Machines, farms of workstations, Cellular Neural Networks, Crays, and other hardware paradigms of parallelism are used by the authors of this collection.The papers arise from the areas of parallel knowledge representation, neural modeling, parallel non-monotonic reasoning, search and partitioning, constraint satisfaction, theorem proving, parallel decision trees, parallel programming languages and low-level computer vision. The final paper is an experience report about applications of massive parallelism which can be said to capture the spirit of a whole period of computing history.This volume provides the reader with a snapshot of the state of the art in Parallel Processing for Artificial Intelligence.
Download or read book Pattern Recognition Architectures Algorithms And Applications written by Rejean Plamondon and published by World Scientific. This book was released on 1991-08-12 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains 15 reviewed papers selected from among those presented at the 4th Vision Interface Conference in Halifax, Canada 14 - 18 May 1990. The papers are grouped into three sections which deal with parallel architectures and neural networks, algorithms for analysis and processing, and systems and applications.
Download or read book Signal Processing written by Willard Miller (Jr) and published by . This book was released on 1989 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: Partial Contents: On the complexity of pattern recognition algorithms on a tree-structured parallel computer; Soliton mathematics in signal processing; The phase problem of X-Ray crystallography; Extension problems under the displacement structure regime; Basic Algorithms in Tomography; Speech recognition using pattern recognition methods. Keywords: Abstracts. (kr).
Download or read book Parallel Image Processing written by M Nivat and published by World Scientific. This book was released on 1992-10-29 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contents:Three-Dimensional Object Pattern Representation by Array Grammars (P S P Wang)Stochastic Puzzle Grammars (R Siromoney et al.)Parallel Recognition of High Dimensional Images (M Nivat & A Saoudi)Two-Dimensional Uniquely Parsable Isometric Array Grammars (Y Yamamoto & K Morita)Replicated Image Algorithms and Their Analyses on SIMD Machines (P J Narayanan & L S Davis)The Depth and Motion Analysis Machine (O D Faugeras et al.)Image Analysis on Massively Parallel Computers: An Architecture Point of View (A Mérigot & B Zavidovique)Parallel Algorithm for Colour Texture Generation Using the Random Neural Network Model (V Atalay & E Gelenbe)and other papers Readership: Computer scientists. keywords:
Download or read book Algorithms and Complexity written by Bozzano G Luisa and published by Elsevier. This book was released on 1990-09-12 with total page 1014 pages. Available in PDF, EPUB and Kindle. Book excerpt: This first part presents chapters on models of computation, complexity theory, data structures, and efficient computation in many recognized sub-disciplines of Theoretical Computer Science.
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1989 with total page 988 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Signal Processing written by Louis Auslander and published by Springer. This book was released on 1990-02-20 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volumes of Signal Processing are based on lectures delivered during a six week program held at the IMA from June 27 to August 5, 1988. The first two weeks of the program dealt with general areas and methods of Signal Pro cessing. The problem areas included imaging and analysis of recognition, x-ray crystallography, radar and sonar, signal analysis and 1-D signal processing, speech, vision, and VLSI implementation. The methods discussed included harmonic anal ysis and wavelets, operator theory, algorithm complexity, filtering and estimation, and inverse scattering. The topics of weeks three and four were digital filter, VLSI implementation, and integrable circuit modelling. In week five the concentration was on robust and nonlinear control with aerospace applications, and in week six the emphasis was on problems in radar, sonar and medical imaging. Because of the large overlap between the various one-week and two-week seg ments of the program, we found it more convenient to divide the material somewhat differently. Part I deals with general signal process theory and Part II deals with (i) application of signal processing, (ii) control theory related themes. We are grateful to the scientific organizers: Tom Kailath (Chairman), Louis Aus lander, F. Alberto Grunbaum, J. William Helton, Pramod P. Khargonekar and Sanjoy K. Mitter. We are also grateful for the generous support given to the IMA program by the Office of Naval Research, the Air Force Office of Scientific Research, the Army Research Office and the National Security Agency.
Download or read book Pattern Matching Algorithms written by Alberto Apostolico and published by Oxford University Press, USA. This book was released on 1997 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues of matching and searching on elementary discrete structures arise pervasively in computer science and many of its applications, and their relevance is expected to grow as information is amassed and shared at an accelerating pace. Several algorithms were discovered as a result of these needs, which in turn created the subfield of Pattern Matching. This book provides an overview of the current state of Pattern Matching as seen by specialists who have devoted years of study to the field. It covers most of the basic principles and presents material advanced enough to faithfully portray the current frontier of research. Because of these recent advances, this is the right time for a book that brings together information relevant to both graduate students and specialists in need of an in-depth reference.
Download or read book PATTERN RECOGNITION written by Syed Thouheed Ahmed and published by MileStone Research Publications. This book was released on 2021-08-01 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the primary and supportive topics on pattern recognition with respect to beginners understand-ability. The aspects of pattern recognition is value added with an introductory of machine learning terminologies. This book covers the aspects of pattern validation, recognition, computation and processing. The initial aspects such as data representation and feature extraction is reported with supportive topics such as computational algorithms and decision trees. This text book covers the aspects as reported. Par t - I In this part, the initial foundation aspects of pattern recognition is discussed with reference to probabilities role in influencing a pattern occurrence, pattern extraction and properties. Introduction: Definition of Pattern Recognition, Applications, Datasets for Pattern Recognition, Different paradigms for Pattern Recognition, Introduction to probability, events, random variables, Joint distributions and densities, moments. Estimation minimum risk estimators, problems. Representation: Data structures for Pattern Recognition, Representation of clusters, proximity measures, size of patterns, Abstraction of Data set, Feature extraction, Feature selection, Evaluation. Par t - II In Part - II of the text, the mathematical representation and computation algorithms for extracting and evaluating patterns are discussed. The basic algorithms of machine learning classifiers with Nearest neighbor and Naive Bayes is reported with value added validation process using decision trees. Computational Algorithms: Nearest neighbor algorithm, variants of NN algorithms, use of NN for transaction databases, efficient algorithms, Data reduction, prototype selection, Bayes theorem, minimum error rate classifier, estimation of probabilities, estimation of probabilities, comparison with NNC, Naive Bayesclassifier, Bayesian belief network. Decision Trees: Introduction, Decision Tree for Pattern Recognition, Construction of Decision Tree, Splittingat the nodes, Over-fitting& Pruning, Examples.
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1982 with total page 1284 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Frontiers in Algorithmics written by Xiaotie Deng and published by Springer Science & Business Media. This book was released on 2009-06-08 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Third International Frontiers of Algorithmics Workshop (FAW 2009), held during June 20–23,2009 at Hefei University of Technology, Hefei, Anhui, China, continued to provide a focused forum on current trends in research on algori- mics,includingdiscretestructures,andtheirapplications.We aimatstimulating the various ?elds for which algorithmics can become a crucial enabler, and to strengthenthe ties between the Easternand Westernalgorithmicsresearchc- munities as well as theory and practice of algorithmics. We had three distinguished invited speakers: Guoliang Chen, Andrew Chi- Chih Yao and Frances Foong Yao, speaking on parallel computing, communication complexity and applications, and computer and network power management. The ?nal program also included 33 peer-reviewed papers selected out of 87 contributed submissions, covering topics including approximation and online - gorithms; computational geometry; graph theory and graph algorithms; games and applications; heuristics; large-scale data mining; machine learning; pattern recognition algorithms; and parameterized algorithms. April 2009 Xiaotie Deng John Hopcroft Jinyun Xue Organization FAW 2009 was organized by Hefei University of Technology, China.
Download or read book Recognising Patterns in Large Data Sets written by Anang Hudaya Muhamad Amin and published by . This book was released on 2010 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advancements in computer architecture, high speed networks, and sensor/data capture technologies have the potential to generate vast amounts of information and bring in new forms of data processing. Unlike the early computations that worked with small chunks of data, contemporary computing infrastructure is able to generate and store large - petabytes - of data for day-to-day operations. These data may arise from high-dimensional images used in medical diagnosis to millions of multi-sensor data collected for the detection of natural events, these large-scale and complex data are increasingly becoming a common phenomenon. This poses a question of whether our ability to recognise and process these data, matches our ability to generate them. This question will be addressed, by looking at the capability of existing recognition schemes to scale up with this outgrowth of data. A different perspective is needed tomeet the challenges posed by the so called data deluge. So this thesis take a view which is somewhat outside the conventional approaches, such as statistical computations and deterministic learning schemes, this research considers the bringing together strengths of high performance and parallel computing to artificial intelligence and machine learning and thus proposes a distributed processing approach for scalable pattern recognition. The research has identified two important issues related to scalability in pattern recognition. These are complexity of learning algorithm and dependency on single processing (CPU-centric) scheme. Scalability in regards to pattern recognition, can be defined as the growth in the capability of pattern recognition algorithms to process large-scale data sets rapidly and with an acceptable level of accuracy. To scale up the recognition process, a pattern recognition system should acquire simple learning mechanisms and the ability to parallelise and distribute its processes for analysis of increasingly large and complex patterns. This thesis describes a new form of pattern recognition by enabling recognition procedure to be synthesised into a large number of loosely-coupled processes, using a fast single-cycle learning associative memory algorithm. This algorithm implements a divide-and-distribute approach on patterns, hence reducing the processing load capacity per compute node. By using this algorithm, patterns arising from diverse sources e.g. high resolution images and sensor readings may be distributed across parallel computational networks for recognition purposes using a generic framework. Furthermore, the approach enables the recognition process to be scaled up for increasing size and dimension of patterns, given sufficient processing capacity available in hand. Apart from this, a single-cycle learning mechanism being applied in this scheme allows recognition to be performed in a fast and responsive manner, without affecting the level of accuracy of the recogniser. The learning mechanism enables memorisation of a pattern within a single pass, therefore, adding more patterns to the scheme does not affect its performance and accuracy. A series of tests have been performed on recognition accuracy and computational complexity using different types of patterns ranging from facial images to sensor readings. This was done to study the accuracy and scalability of the distributed pattern recognition scheme. The results of these analyses have indicated that the proposed scheme is highly scalable, enables fast/online learning, and is able to achieve accuracy that is comparable to well known machine learning techniques.After addressing the scalability and performance aspects, this thesis deals with pattern complexity by including pattern recognition applications with multiple features. With the recognition process implemented in a distributed manner, the capacity for allowing more features to be added is possible. The proposed multi-feature approach provides an effective scheme that is capable to accommodate multiple pattern features within the analysis process. This is essential in data mining applications that involve complex data, such as biomedical images containing numerous features. The distributed multi-feature approach using single-cycle learning algorithm demonstrates high recall accuracy in the recognition simulations involving complex images.Finally, this thesis investigates the scheme's adaptability to different levels of network granularity and discovers important factors for the scalability of the pattern recognition scheme. This allows the recognition scheme to be deployed in different network conditions, ranging from coarse-grained networks such as computational grids, to fine-grained systems, including wireless sensor networks (WSNs). By acquiring resource-awareness, the proposed distributed pattern recogniser can be deployed in different kinds of applications on different network platforms, creating a generic scheme for pattern recognition. Further analysis on adaptive network granularity feature of distributed single-cycle learning pattern recognition scheme was conducted as a case study to examine the effectiveness and efficiency of the proposed approach for distributed event detection within fine-grained WSN networks. The outcomes of the study indicate that the distributed pattern recognition approach is well-suited for performing event detection using the divide-and-distribute approach with the in-network parallel processing mechanism within a resource-constrained environment. Furthermore, the ability to perform recognition using a simple learning mechanism, enables each sensor node to perform complex applications such as event detection. As a result, this research may give a new insight for applications involving large-scale event detection including forest-fire detection and structural health monitoring (SHM) for mega-structures.
Download or read book Image Understanding Workshop written by United States. Defense Advanced Research Projects Agency. Information Science and Technology Office and published by . This book was released on 1987 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Pattern Recognition and Computer Vision 5th Edition written by Chi-hau Chen and published by World Scientific. This book was released on 2015-12-15 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides an up-to-date and authoritative treatment of pattern recognition and computer vision, with chapters written by leaders in the field. On the basic methods in pattern recognition and computer vision, topics range from statistical pattern recognition to array grammars to projective geometry to skeletonization, and shape and texture measures. Recognition applications include character recognition and document analysis, detection of digital mammograms, remote sensing image fusion, and analysis of functional magnetic resonance imaging data, etc.
