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 Environment -- Abstract -- 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 Microcontroller -- 4. 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 -Search -- with 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 -- 4. Producing Radial Color Moments -- 5. Radial Moment Invariants -- 6. Computational Aspects & Algorithms -- 7. Experimental Analysis -- Conclusion -- References -- Chapter 6: Pattern Recognition by Bessel Mask and One-Dimensional Signatures -- Abstract -- 1. The Bessel Mask -- 2. The One-Dimensional Signature -- 3. The Pattern Recognition -- Acknowledgments -- References -- Index.