Download or read book Scalable Pattern Recognition Algorithms written by Pradipta Maji and published by Springer Science & Business Media. This book was released on 2014-03-19 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.

Download or read book Internet Scale Pattern Recognition written by Anang Hudaya Muhamad Amin and published by CRC Press. This book was released on 2012-11-20 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: For machine intelligence applications to work successfully, machines must perform reliably under variations of data and must be able to keep up with data streams. Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds unveils computational models that address performance and scalability to achieve higher levels of reliability. It explores different ways of implementing pattern recognition using machine intelligence. Based on the authors’ research from the past 10 years, the text draws on concepts from pattern recognition, parallel processing, distributed systems, and data networks. It describes fundamental research on the scalability and performance of pattern recognition, addressing issues with existing pattern recognition schemes for Internet-scale data deployment. The authors review numerous approaches and introduce possible solutions to the scalability problem. By presenting the concise body of knowledge required for reliable and scalable pattern recognition, this book shortens the learning curve and gives you valuable insight to make further innovations. It offers an extendable template for Internet-scale pattern recognition applications as well as guidance on the programming of large networks of devices.

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 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 Pattern Recognition and Machine Learning written by Christopher M. Bishop and published by Springer. This book was released on 2016-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Download or read book Applied Pattern Recognition written by Dietrich Paulus and published by Springer Science & Business Media. This book was released on 2003-02-25 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book demonstrates the efficiency of the C++ programming language in the realm of pattern recognition and pattern analysis. For this 4th edition, new features of the C++ language were integrated and their relevance for image and speech processing is discussed.

Download or read book Scaling Up Machine Learning written by Ron Bekkerman and published by Cambridge University Press. This book was released on 2012 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Download or read book Internet Scale Pattern Recognition written by Anang Muhamad Amin and published by CRC Press. This book was released on 2012-11-20 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: For machine intelligence applications to work successfully, machines must perform reliably under variations of data and must be able to keep up with data streams. Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds unveils computational models that address performance and scalability to achieve higher levels

Download or read book Artificial Intelligence and Soft Computing written by Leszek Rutkowski and published by Springer. This book was released on 2013-06-04 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 7894 and LNCS 7895 constitutes the refereed proceedings of the 12th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2013, held in Zakopane, Poland in June 2013. The 112 revised full papers presented together with one invited paper were carefully reviewed and selected from 274 submissions. The 57 papers included in the first volume are organized in the following topical sections: neural networks and their applications; fuzzy systems and their applications; pattern classification; and computer vision, image and speech analysis.

Download or read book Scalable Non Parametric Pattern Recognition Techniques for Data Mining written by Suresh Veluru and published by . This book was released on 2011-08 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Scalable Optimization via Probabilistic Modeling written by Martin Pelikan and published by Springer. This book was released on 2007-01-12 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.

Download or read book Scalable Fuzzy Algorithms for Data Management and Analysis Methods and Design written by Laurent, Anne and published by IGI Global. This book was released on 2009-10-31 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents up-to-date techniques for addressing data management problems with logic and memory use"--Provided by publisher.

Download or read book Scalable Information Systems written by Peter Mueller and published by Springer. This book was released on 2009-11-16 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: In view of the incessant growth of data and knowledge and the continued diversifi- tion of information dissemination on a global scale, scalability has become a ma- stream research area in computer science and information systems. The ICST INFO- SCALE conference is one of the premier forums for presenting new and exciting research related to all aspects of scalability, including system architecture, resource management, data management, networking, and performance. As the fourth conf- ence in the series, INFOSCALE 2009 was held in Hong Kong on June 10 and 11, 2009. The articles presented in this volume focus on a wide range of scalability issues and new approaches to tackle problems arising from the ever-growing size and c- plexity of information of all kind. More than 60 manuscripts were submitted, and the Program Committee selected 22 papers for presentation at the conference. Each s- mission was reviewed by three members of the Technical Program Committee.

Download or read book Sampling Techniques for Supervised or Unsupervised Tasks written by Frédéric Ros and published by Springer Nature. This book was released on 2019-10-26 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the “curse of dimensionality”, their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the field and discusses the state of the art concerning sampling techniques for supervised and unsupervised task. Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks; Describe implementation and evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality; Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. "This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge." M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas "In science the difficulty is not to have ideas, but it is to make them work" From Carlo Rovelli

Download or read book Digital Image Computing Techniques and Applications written by Changming Sun and published by CSIRO PUBLISHING. This book was released on 2003-12-01 with total page 916 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital Image Computing: Techniques and Applications is the premier biennial conference in Australia on the topics of image processing and image analysis. This seventh edition of the proceedings has seen an unprecedented level of submission, on such diverse areas as: Image processing; Face recognition; Segmentation; Registration; Motion analysis; Medical imaging; Object recognition; Virtual environments; Graphics; Stereo-vision; and Video analysis. These two volumes contain all the 108 accepted papers and five invited talks that were presented at the conference. These two volumes provide the Australian and international imaging research community with a snapshot of current theoretical and practical developments in these areas. They are of value to any engineer, computer scientist, mathematician, statistician or student interested in these matters.

Download or read book Machine Learning and Data Mining in Pattern Recognition written by Petra Perner and published by Springer. This book was released on 2017-07-01 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017, held in New York, NY, USA in July/August 2017.The 31 full papers presented in this book were carefully reviewed and selected from 150 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

Download or read book Statistical Pattern Recognition written by Andrew R. Webb and published by Newnes. This book was released on 1999 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides an introduction to statistical pattern recognition theory and techniques. Most of the material presented in this book is concerned with discrimination and classification and has been drawn from a wide range of literature including that of engineering, statistics, computer science and the social sciences. This book is an attempt to provide a concise volume containing descriptions of many of the most useful of today's pattern processing techniques including many of the recent advances in nonparametric approaches to discrimination developed in the statistics literature and elsewhere. The techniques are illustrated with examples of real-world applications studies. Pointers are also provided to the diverse literature base where further details on applications, comparative studies and theoretical developments may be obtained"--Page [xv].