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Pattern Recognition: Statistical and Neural

Nanjing University of Science & Technology. Pattern Recognition: Statistical and Neural. Lonnie C. Ludeman Lecture 2. Motivation. Motivation. Motivation. Simple Problem (Isolated Objects). Slightly Harder Problem( Arbitrary orientation). Harder Problem: ( Occluded objects).

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Pattern Recognition: Statistical and Neural

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  1. Nanjing University of Science & Technology Pattern Recognition:Statistical and Neural Lonnie C. Ludeman Lecture 2

  2. Motivation Motivation Motivation

  3. Simple Problem (Isolated Objects)

  4. Slightly Harder Problem( Arbitrary orientation)

  5. Harder Problem: ( Occluded objects)

  6. Pattern Recognition

  7. Pattern Classification ?

  8. Hardest Problem Random orientation Random size Three dimensional Occluded Noisy

  9. Humans Classify objects from observations obtained from sensory inputs: Visual, Auditory, taste, temperature, pressure Adults easily classify a moving vehicle as to size, type, and speed and classify plants and animals Infants(1 year old) recognizes : balls, books, kittens, toys, spoken words. Babies(3 months old) recognizes : faces, toys, heat, cold, pressure and lights

  10. These tasks of classification from sensory inputs are easy for humans Also the limulous crab can recognize and respond to optical stimulus.

  11. Question : How do humans and animals perform these complex problems? Answer: The processes are not very well understood!

  12. Research for the Pattern Recognition Problem Suggest Research on anatomy and physiology Mathematical research in types of processing Research on simpler Biological systems Computer implementation Human Brains Human Brains Understand Provide clues

  13. Purpose of this course: Not try to imitate the human brain Present some Basic Framework Include General methods for solving the special problems of Pattern classification Pattern recognition Learn how to crawl !!

  14. Methods for Recognitionof Patterns • K-means algorithm • Hierarchical clustering • Fuzzy clustering • Adaptive clustering

  15. K-Means Algorithm Works on Quantitative DATA Results depend on distance measures used Non unique results Results for fixed number of classes May converge to local minimum

  16. HierarchicalClustering Used on Quantitative data Can be modified to be used on nonquantitative data by using similarity measures Obtains clustering for all number of classes in process With large number of samples results are difficult to interpret Once in cluster always in cluster Non unique results

  17. Fuzzy Clustering Converges rapidly Can be used on non quantitative Data Can be used for hard clustering of data Can learn membership functions Non unique solution

  18. Adaptive Clustering ISODATA Algorithm ART-1 and Art-2

  19. General Methods for Classification • Statistical • Neural networks • Syntactical • Structural (Ad Hoc) • Others

  20. Statistical Method Information Required: knowledge of conditional probability densities, apriori probabilities, performance measures Tools: Classical Statistical decision theory Advantages: Can obtain optimum decision rule for a given performance measure Disadvantages: Requires considerable statistical information, rigid solution, sometimes too complicated

  21. Neural Networks • Information Required: Sets of classified training pattterns • Tools: Nonlinear analysis, functional approximation • Advantages: Can solve very complex problems • Disadvantages: Not easily changed, may be a complex implementation, may not converge, sometimes too complicated , does not provide any structural knowledge

  22. Syntactical Method Information Required: Grammars for the patterns to classify Tools: Language and grammar Theory, parsing methods, machine theoryAdvantages: Can solve some non numerical type problemsDisadvantages: Grammars difficult to formulate, Overlapping grammars

  23. Information Required: knowledge of structural properties of dataTools: Linear and nonlinear Discriminant functionsAdvantages: Can obtain simple and strait forward solutions, easy to changeDisadvantages: Requires considerable insight to the problem, changes in characteristics may require a complete redesign, each problemcould require different types of solutions. Structural Methods (Ad Hoc)

  24. Other Approaches Fuzzy neural networks Fuzzy data representation

  25. Keys to Pattern Recognition and Pattern Classification are Learning at many different levels. Clear statement of problems Use different tools for different problems Use Hybrid methods Understand your data

  26. Words of Wisdom Do not force one method on all problems Complex problems may require hybrid type approaches

  27. If you play cards with someone you do not know do not play for money!!!

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