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Pattern recognition : practices, perspectives, and challenges / Darrell B. Vincent, editor.

Contributor(s): Material type: TextTextSeries: Computer science, technology and applicationsPublisher: Hauppauge. New York : Nova Science Publishers, Inc., [2013]Description: 1 online resource (x, 192 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781626181984
  • 1626181985
Subject(s): Genre/Form: Additional physical formats: Print version:: Pattern recognitionDDC classification:
  • 006.4 23
LOC classification:
  • TK7882.P3 P3965 2013
Online resources:
Contents:
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.
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.
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.
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.
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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Electronic-Books Electronic-Books OPJGU Sonepat- Campus E-Books EBSCO Available

Includes bibliographical references and index.

Print version record.

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.

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.

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.

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.

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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