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Introduction to Pattern Recognition

Introduction to Pattern Recognition. What was that….?. Recognition Or Classification. Recognition Etymologically, the act of thinking again Involves “identifying” or “acknowledging” Classification Etymologically, the act of separating into groups

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Introduction to Pattern Recognition

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  1. Introduction to Pattern Recognition What was that….?

  2. Recognition Or Classification • Recognition • Etymologically, the act of thinking again • Involves “identifying” or “acknowledging” • Classification • Etymologically, the act of separating into groups • Involves “sorting” according to what a thing is called or “associating” to a group Copyright, G. A. Tagliarini, PhD

  3. Recognition Rigel (900 ly) Betelgeuse (300 ly) Copyright, G. A. Tagliarini, PhD

  4. Input source Sensing Segmentation Feature Extraction Recognition The Classification Process System Response Copyright, G. A. Tagliarini, PhD

  5. Sensing • Depends on the application domain • Consistency can vary widely within and across domains • Must result in a basis for measuring discriminatory features—distinguishing characteristics must be “observable” Copyright, G. A. Tagliarini, PhD

  6. Segmentation: Extremely Challenging • A required preprocessing step • Examples: • What is the basis for separating components of an image? Color, proximity, boundary contours, “texture” • Where are the boundaries between handwritten letters or words? • When does a spoken word start/stop? Copyright, G. A. Tagliarini, PhD

  7. Feature Extraction • What features are salient for the classification? • Are the features robust? • Do they vary with parameters such as time, frequency, scale, translation, rotation, or proximity? • Do subsets of the features provide classification efficacy? Copyright, G. A. Tagliarini, PhD

  8. Classification • What are the classifier design objectives? • Minimize classification error(s) • Type 1 (reject a true Ho) • Type 2 (fail to reject a false Ho) • Generalization • Reduced computational complexity • Reduced algorithmic complexity • Noise Copyright, G. A. Tagliarini, PhD

  9. System Response • So what? Copyright, G. A. Tagliarini, PhD

  10. Machine Learning: Creating a Classifier Adaptively • Supervised learning • Feedforward network and backpropagation • Hopfield • Unsupervised learning • ART • Kohonen Copyright, G. A. Tagliarini, PhD

  11. Some Sample Problems • Intrusion detection in network traffic • Handwritten character/word recognition • Speech recognition • Sonar acoustic transient recognition • Face recognition • Fingerprint classification Copyright, G. A. Tagliarini, PhD

  12. Key Questions • What are the examples? (data) • What characteristics distinguish the class exemplars? (features) • How will discriminatory evidence be combined to make a decision? (classifier) • How well does it work? (assessment) Copyright, G. A. Tagliarini, PhD

  13. Classifier Construction • Data collection or generation • Data may not be abundant or available • Identify features • Determines preprocessing requirements • Choose a classifier to implement • Model may prescribe the classifier • Model may require adaptive construction (training) • Performance Assessment Copyright, G. A. Tagliarini, PhD

  14. No Free Lunch Theorem • Loosely stated, “There is no classifier model that will be optimal for all classification problems.” Copyright, G. A. Tagliarini, PhD

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