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Pattern recognition : practices, perspectives, and challenges /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Vincent, Darrell B.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hauppauge. New York : Nova Science Publishers, Inc., [2013]
Colección:Computer science, technology and applications.
Temas:
Acceso en línea:Texto completo

MARC

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245 0 0 |a Pattern recognition :  |b practices, perspectives, and challenges /  |c Darrell B. Vincent, editor. 
264 1 |a Hauppauge. New York :  |b Nova Science Publishers, Inc.,  |c [2013] 
300 |a 1 online resource (x, 192 pages) :  |b illustrations. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Computer science, technology and applications 
504 |a Includes bibliographical references and index. 
588 0 |a Print version record. 
505 0 |a PATTERN RECOGNITION: PRACTICES, PERSPECTIVES AND CHALLENGES; PATTERN RECOGNITION: PRACTICES, PERSPECTIVES AND CHALLENGES; Library of Congress Cataloging-in-Publication Data; Contents; Preface; Chapte 1: Pattern Recognition Applied to Spectroscopy: Conventional Methods and Future Directions; 1PPGCA Universidade Federal de Minas Gerais; Belo Horizonte, MG -- Brasil; 2DEMEC, Universidade Federal de Minas Gerais; Belo Horizonte, MG -- Brasil; Abstract; 1. Introduction; 2. Basic Concepts of Spectroscopy; 3. Classification Techniques; 3.1. Unsupervised Analysis. 
505 8 |a 3.1.1. Principal Component Analysis (PCA)3.1.2. Clustering Algorithms; 3.2. Supervised Learning; 3.2.1. Linear Discriminant Analysis (LDA); 3.2.2. Partial Least Squares Discriminant Analysis; 3.2.3. kNN (k-Nearest Neighbor); 3.2.4. Neural Networks; 4. Regression Analysis; 4.1. Linear Regression Analysis; 4.2. Non-Linear Regression Analysis; 5. Future Trends; 5.1. Sparse Learning Dimensionality Reduction Algorithms; 5.2. Hyperspectral Analysis; 5.2.1. Support Vector Machines; Concluding Remarks; Acknowledgments; References. 
505 8 |a Chapter 2: Optimization of an Embedded Simplified Fuzzy ARTMAP Implemented on a Microcontroller Using MATLAB GUI EnvironmentAbstract; 1. Introduction; 2. Fuzzy ARTMAP: A Brief Review; 2.1. Simplified Fuzzy ARTMAP; 2.2. Simplified Fuzzy ARTMAP Graphical User Interface; 2.2.1. Input Data; 2.2.2. Training; 2.2.3. Cross Validation; 2.2.4. Data Selection; 3. Materials and Methods[24]-[73]; 3.1. Electrodes; 3.2. Electronic System; 3.3. Measurement Process; 3.4. Data Analysis; 3.4.1. Training and Validation with GUI; Floral Origin Network; Physical Treatment Network. 
505 8 |a 3.5. Implementation of SFAM in the Microcontroller4. Results and Discussion; Conclusion; Acknowledgments; References; Chapter 3: Application of Pattern Recognition in Optimization-Simulation Technique; Abstract; Introduction; 1. Optimization with Simulation Model Using; 2. Classification of Optimization-Simulation Problems; 2.1. Single-Criterion Problems; 2.2. Multicriteria Problems; 2.3. Optimization Problem with Continuous Optimization Criterion; 3. Algorithm for the Efficiency Region Searching; 3.1. Application Pattern Recognition Methods in the Algorithm of Efficiency Region Search. 
505 8 |a 4. Examples of Application -Searchwith Averaging; Conclusion; References; Chapter 4: Practical Usage of Algorithmic Probability in Pattern Recognition; Abstract; 1. Introduction; 2. Bayes' Criterion; 3. Practical Minimum; Description Length Principle; 4. Algorithmic Complexity and Probability; 5. Between Theoretical and Practical MDL; 6. Algorithmic Probability; 7. Towards Practical Algorithmic Probability; Conclusion; References; Chapter 5: Pattern Recognition Using Quaternion Color Moments; Abstract; 1. Introduction; 2. Quaternion Basics; 3. Moment Categories. 
590 |a eBooks on EBSCOhost  |b EBSCO eBook Subscription Academic Collection - Worldwide 
650 0 |a Pattern recognition systems. 
650 2 |a Pattern Recognition, Automated 
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650 7 |a Pattern recognition systems.  |2 fast  |0 (OCoLC)fst01055266 
700 1 |a Vincent, Darrell B. 
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830 0 |a Computer science, technology and applications. 
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