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|a UAMI
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| 245 |
0 |
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|a Pattern recognition :
|b practices, perspectives, and challenges /
|c Darrell B. Vincent, editor.
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| 264 |
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1 |
|a Hauppauge. New York :
|b Nova Science Publishers, Inc.,
|c [2013]
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| 300 |
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|a 1 online resource (x, 192 pages) :
|b illustrations.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
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| 490 |
1 |
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|a Computer science, technology and applications
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| 504 |
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|a Includes bibliographical references and index.
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0 |
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|a Print version record.
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|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.
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| 505 |
8 |
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|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.
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| 505 |
8 |
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|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.
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| 505 |
8 |
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|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.
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| 505 |
8 |
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|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.
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|a Pattern recognition systems.
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|a Pattern Recognition, Automated
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|a Reconnaissance des formes (Informatique)
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|a COMPUTERS
|x Optical Data Processing.
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