Pattern recognition in industry / Phiroz Bhagat.

Format:

Book

Author/Creator:

Bhagat, Phiroz.

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Pattern recognition systems.

Physical Description:

1 online resource (201 p.)

Edition:

1st ed.

Place of Publication:

Boston : Elsevier, c2005.

Language Note:

English

Summary:

""Find it hard to extract and utilise valuable knowledge from the ever-increasing data deluge?"" If so, this book will help, as it explores pattern recognition technology and its concomitant role in extracting useful information to build technical and business models to gain competitive industrial advantage.*Based on first-hand experience in the practice of pattern recognition technology and its development and deployment for profitable application in Industry.Phiroz Bhagat is often referred to as the pioneer of neural net and pattern recognition technology, a

Contents:

Front Cover; Pattern Recognition in Industry; Copyright Page; Contents; Preface; Acknowledgments; About the Author; Part I: Philosophy; Chapter 1. Introduction; 1.1. Distinguishing Knowledge and Information from Data; 1.2. Whence Pattern Recognition Technology; 1.3. Thermodynamic Concept of Order Leading to Information Theory; 1.4. Modeling Informed by Observation; 1.5. Pattern Recognition Technology Triad; References; Chapter 2. Patterns Within Data; 2.1. Types of Data; 2.2. Characterizing Data; 2.3. Distance Between Data; 2.4. Organizing Data-Clustering / Auto-Classification

2.5. Organizing Data-Data Series Resonance2.6. Organizing Data-Correlative Modeling; References; Chapter 3. Adapting Biological Principles for Deployment in Computational Science; 3.1. Learning Organisms-An Introduction to Neural Nets; 3.2. Supervised Learning; 3.3. Unsupervised Learning; 3.4. Models that Self-Organize Data (Unsupervised Learning) as well as Correlate them with Dependent Outcomes (Supervised Learning); 3.5. Genetic Algorithms; References; Chapter 4. Issues in Predictive Empirical Modeling; 4.1. Pre-Conditioning Data: Pre- and Post-Processing

4.2. Detecting Extrapolative Conditions4.3. Embedding Mechanistic Understanding / Experiential Judgment to Enhance Extrapolative Robustness; 4.4. Insight into Model Behavior; Part II: Technology; Chapter 5. Supervised Learning-Correlative Neural Nets; 5.1. Supervised Learning with Back-Propagation Neural Nets; 5.2. Feedforward-Exercising the BP Net in Predictive Mode-Neuron Transformation Function; 5.3. BP Training-Connection Weights Adjusted by the "Delta Rule" to Minimize Learning Errors; 5.4. Back-Propagation Equations for General Transformation Functions

5.5. Back-Propagation Equations for Sigmoidal Transformation Functions5.6. Conjugate Gradient Methodology for Rapid and Robust Convergence; 5.7. Separating Signal from Noise in Training; 5.8. Pre-Conditioning Data for BP Nets; 5.9. Supervised Learning with Radial Basis Function Neural Nets; 5.10. Seeding the Input Data Space with RBF Cluster Centers; 5.11. Assigning Spheres of Influence to each Cluster; 5.12. Activating Clusters from a Point in the Data Space; 5.13. Developing RBF Correlation Models-Assigning Weights to Map Outcome; 5.14. Pre-Conditioning Data for RBF Nets

5.15. Neural Net Correlation ModelsReferences; Chapter 6. Unsupervised Learning: Auto-Clustering and Self-Organizing Data; 6.1. Unsupervised Learning-Value to Industry; 6.2. Auto-Clustering Using Radial Basis Functions; 6.3. RBF Cluster Radius; 6.4. Competitive Learning; 6.5. Data Pre-Conditioning for Competitive Learning; References; Chapter 7. Customizing for Industrial Strength Applications; 7.1. Modeling: The Quest for Explaining and Predicting Processes; 7.2. Combining Empiricism with Mechanistic Understanding; 7.3. Embedding an Idealized (Partially Correct) Model

7.4. Embedding A Priori Understanding in the Form of Constraints

Notes:

Description based upon print version of record.

ISBN:

1-280-63015-9

9786610630158

0-08-045602-2

OCLC:

476000499