Pattern Recognition Approach to Data Interpretation

Pattern Recognition Approach to Data Interpretation PDF Author: Diane Wolff
Publisher: Springer Science & Business Media
ISBN: 146159331X
Category : Computers
Languages : en
Pages : 226

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Book Description
An attempt is made in this book to give scientists a detailed working knowledge of the powerful mathematical tools available to aid in data interpretation, especially when con fronted with large data sets incorporating many parameters. A minimal amount of com puter knowledge is necessary for successful applications, and we have tried conscien tiously to provide this in the appropriate sections and references. Scientific data are now being produced at rates not believed possible ten years ago. A major goal in any sci entific investigation should be to obtain a critical evaluation of the data generated in a set of experiments in order to extract whatever useful scientific information may be present. Very often, the large number of measurements present in the data set does not make this an easy task. The goals of this book are thus fourfold. The first is to create a useful reference on the applications of these statistical pattern recognition methods to the sciences. The majority of our discussions center around the fields of chemistry, geology, environmen tal sciences, physics, and the biological and medical sciences. In Chapter IV a section is devoted to each of these fields. Since the applications of pattern recognition tech niques are essentially unlimited, restricted only by the outer limitations of.

Pattern Recognition Approach to Data Interpretation

Pattern Recognition Approach to Data Interpretation PDF Author: Diane Wolff
Publisher: Springer Science & Business Media
ISBN: 146159331X
Category : Computers
Languages : en
Pages : 226

Get Book Here

Book Description
An attempt is made in this book to give scientists a detailed working knowledge of the powerful mathematical tools available to aid in data interpretation, especially when con fronted with large data sets incorporating many parameters. A minimal amount of com puter knowledge is necessary for successful applications, and we have tried conscien tiously to provide this in the appropriate sections and references. Scientific data are now being produced at rates not believed possible ten years ago. A major goal in any sci entific investigation should be to obtain a critical evaluation of the data generated in a set of experiments in order to extract whatever useful scientific information may be present. Very often, the large number of measurements present in the data set does not make this an easy task. The goals of this book are thus fourfold. The first is to create a useful reference on the applications of these statistical pattern recognition methods to the sciences. The majority of our discussions center around the fields of chemistry, geology, environmen tal sciences, physics, and the biological and medical sciences. In Chapter IV a section is devoted to each of these fields. Since the applications of pattern recognition tech niques are essentially unlimited, restricted only by the outer limitations of.

Pattern Recognition Approach to Data Interpretation Data Interpretation

Pattern Recognition Approach to Data Interpretation Data Interpretation PDF Author: Michael Loewen Parsons
Publisher:
ISBN:
Category : Experimental design
Languages : en
Pages :

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Book Description


Pattern Recognition Algorithms for Data Mining

Pattern Recognition Algorithms for Data Mining PDF Author: Sankar K. Pal
Publisher: CRC Press
ISBN: 1135436401
Category : Computers
Languages : en
Pages : 275

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Book Description
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, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

Pattern Recognition

Pattern Recognition PDF Author: Wladyslaw Homenda
Publisher: John Wiley & Sons
ISBN: 1119302838
Category : Technology & Engineering
Languages : en
Pages : 312

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Book Description
A new approach to the issue of data quality in pattern recognition Detailing foundational concepts before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining. Top-down organization presents detailed applications only after methodological issues have been mastered, and step-by-step instructions help ensure successful implementation of new processes. By positioning data quality as a factor to be dealt with rather than overcome, the framework provided serves as a valuable, versatile tool in the analysis arsenal. For decades, practical need has inspired intense theoretical and applied research into pattern recognition for numerous and diverse applications. Throughout, the limiting factor and perpetual problem has been data—its sheer diversity, abundance, and variable quality presents the central challenge to pattern recognition innovation. Pattern Recognition: A Quality of Data Perspective repositions that challenge from a hurdle to a given, and presents a new framework for comprehensive data analysis that is designed specifically to accommodate problem data. Designed as both a practical manual and a discussion about the most useful elements of pattern recognition innovation, this book: Details fundamental pattern recognition concepts, including feature space construction, classifiers, rejection, and evaluation Provides a systematic examination of the concepts, design methodology, and algorithms involved in pattern recognition Includes numerous experiments, detailed schemes, and more advanced problems that reinforce complex concepts Acts as a self-contained primer toward advanced solutions, with detailed background and step-by-step processes Introduces the concept of granules and provides a framework for granular computing Pattern recognition plays a pivotal role in data analysis and data mining, fields which are themselves being applied in an expanding sphere of utility. By facing the data quality issue head-on, this book provides students, practitioners, and researchers with a clear way forward amidst the ever-expanding data supply.

Applications of Pattern Recognition

Applications of Pattern Recognition PDF Author: King-Sun Fu
Publisher: CRC Press
ISBN: 1351086707
Category : Technology & Engineering
Languages : en
Pages : 392

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Book Description
This monograph is intended to cover several major applications of pattern recognition. After a brief introduction to pattern recognition in Chapter 1, the two major approaches, statistical approach and syntactic approach, are reviewed in Chapter 2, and 3, respectively. Other topics include the application of pattern recognition to seismic wave interpretation, to system reliability problems, to medical data analysis, as well as character and speech recognition.

Data Analysis and Pattern Recognition in Multiple Databases

Data Analysis and Pattern Recognition in Multiple Databases PDF Author: Animesh Adhikari
Publisher: Springer Science & Business Media
ISBN: 3319034103
Category : Technology & Engineering
Languages : en
Pages : 247

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Book Description
Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.

Machine Interpretation of Patterns

Machine Interpretation of Patterns PDF Author: Rajat K. De
Publisher: World Scientific
ISBN: 9814299197
Category : Computers
Languages : en
Pages : 316

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Book Description
1. Combining information with a Bayesian multi-class multi-kernel pattern recognition machine / T. Damoulas and M.A. Girolami -- 2. Image quality assessment based on weighted perceptual features / D.V. Rao and L.P. Reddy -- 3. Quasi-reversible two-dimension fractional differentiation for image entropy reduction / A. Nakib [und weitere] -- 4. Parallel genetic algorithm based clustering for object and background classification / P. Kanungo, P.K. Nanda and A. Ghosh -- 5. Bipolar fuzzy spatial information : first operations in the mathematical morphology setting / I. Bloch -- 6. Approaches to intelligent information retrieval / G. Pasi -- 7. Retrieval of on-line signatures / H.N. Prakash and D.S. Guru -- 8. A two stage recognition scheme for offline handwritten Devanagari Words / B. Shaw and S.K. Parui -- 9. Fall detection from a video in the presence of multiple persons / V. Vishwakarma, S. Sural and C. Mandal -- 10. Fusion of GIS and SAR statistical features for earthquake damage mapping at the block scale / G. Trianni [und weitere] -- 11. Intelligent surveillance and Pose-invariant 2D face classification / B.C. Lovell, C. Sanderson and T. Shan -- 12. Simple machine learning approaches to safety-related systems / C. Moewes, C. Otte and R. Kruse -- 13. Nonuniform multi level crossings for signal reconstruction / N. Poojary, H. Kumar and A. Rao -- 14. Adaptive web services brokering / K.M. Gupta and D.W. Aha -- 15. Granular support vector machine based method for prediction of solubility of proteins on over expression in Escherichia Coli and breast cancer classification / P. Kumar, B.D. Kulkarni and V.K. Jayaraman

Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery

Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery PDF Author: Wang, Hsiao-Fan
Publisher: IGI Global
ISBN: 159904983X
Category : Education
Languages : en
Pages : 366

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Book Description
Pattern Recognition has a long history of applications to data analysis in business, military and social economic activities. While the aim of pattern recognition is to discover the pattern of a data set, the size of the data set is closely related to the methodology one adopts for analysis. Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery tackles those data sets and covers a variety of issues in relation to intelligent data analysis so that patterns from frequent or rare events in spatial or temporal spaces can be revealed. This book brings together current research, results, problems, and applications from both theoretical and practical approaches.

Pattern Recognition

Pattern Recognition PDF Author: William Gibson
Publisher: Penguin UK
ISBN: 0141904461
Category : Fiction
Languages : en
Pages : 419

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Book Description
'Part-detective story, part-cultural snapshot . . . all bound by Gibson's pin-sharp prose' Arena -------------- THE FIRST NOVEL IN THE BLUE ANT TRILIOGY - READ ZERO HISTORY AND SPOOK COUNTRY FOR MORE Cayce Pollard has a new job. She's been offered a special project: track down the makers of an addictive online film that's lighting up the internet. Hunting the source will take her to Tokyo and Moscow and put her in the sights of Japanese hackers and Russian Mafia. She's up against those who want to control the film, to own it - who figure breaking the law is just another business strategy. The kind of people who relish turning the hunter into the hunted . . . A gripping spy thriller by William Gibson, bestselling author of Neuromancer. Part prophesy, part satire, Pattern Recognition skewers the absurdity of modern life with the lightest and most engaging of touches. Readers of Neal Stephenson, Ray Bradbury and Iain M. Banks won't be able to put this book down. -------------- 'Fast, witty and cleverly politicized' Guardian 'A big novel, full of bold ideas . . . races along like an expert thriller' GQ 'Dangerously hip. Its dialogue and characterization will amaze you. A wonderfully detailed, reckless journey of espionage and lies' USA Today 'A compelling, humane story with a sympathetic heroine searching for meaning and consolation in a post-everything world' Daily Telegraph 'Electric, profound. Gibson's descriptions of Tokyo, Russia and London are surreally spot-on' Financial Times

Data Complexity in Pattern Recognition

Data Complexity in Pattern Recognition PDF Author: Mitra Basu
Publisher: Springer Science & Business Media
ISBN: 1846281725
Category : Computers
Languages : en
Pages : 309

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Book Description
Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.