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

2. 2 Motivation

3. 3 Simple Problem (Isolated Objects)

4. 4 Slightly Harder Problem( Arbitrary orientation)

5. 5 Harder Problem: ( Occluded objects)

6. 6 Pattern Recognition

7. 7 Pattern Classification ?

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

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11. 11 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

12. 12 These tasks of classification from sensory inputs are easy for humans Also the limulous crab can recognize and respond to optical stimulus.

13. 13 Question : How do humans and animals perform these complex problems? Answer: The processes are not very well understood!

14. 14 Research on anatomy and physiology Mathematical research in types of processing Research on simpler Biological systems Computer implementation Human Brains Suggest Understand Provide clues Research for the Pattern Recognition Problem

15. 15 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 !!

16. 16 Methods for Recognition of Patterns K-means algorithm Hierarchical clustering Fuzzy clustering Adaptive clustering

17. 17 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

18. 18 Hierarchical Clustering 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

19. 19 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

20. 20 Adaptive Clustering ISODATA Algorithm ART-1 and Art-2

21. 21 General Methods for Classification Statistical Neural networks Syntactical Structural (Ad Hoc) Others

22. 22 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

23. 23 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

24. 24 Information Required: Grammars for the patterns to classify Tools: Language and grammar Theory, parsing methods, machine theory Advantages: Can solve some non numerical type problems Disadvantages: Grammars difficult to formulate, Overlapping grammars Syntactical Method

25. 25 Information Required: knowledge of structural properties of data Tools: Linear and nonlinear Discriminant functions Advantages: Can obtain simple and strait forward solutions, easy to change Disadvantages: 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)

26. 26 Other Approaches Fuzzy neural networks Fuzzy data representation

27. 27 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

28. 28 Words of Wisdom Do not force one method on all problems Complex problems may require hybrid type approaches

29. 29 If you play cards with someone you do not know do not play for money!!!