Pattern Recognition: Statistical and Neural

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Pattern RecognitionStatistical and Neural
Nanjing University of Science Technology

• Lonnie C. Ludeman
• Lecture 3
• Sept 16, 2005

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Review 1
Basic Pattern Classification Approaches Statisti
cal Syntactical Neural Networks Fuzzy
System Ad Hoc Procedures
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Review 2
Pattern Recognition Approaches K-Means
Clustering Hierarical Clustering Fuzzy
Clustering Adaptive Clustering
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Review 3
DO NOT force the same Solution on all problems
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Lecture 3 Topics
1. Classification Examples 2. Basic Pattern
Classification Structure 3. Statistical Pattern
Classification
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Example 1 Character Classification
Consider the classification of Binary Images
of letters a and b of different
fonts Given a black and white letter Classify
it as coming from Class A or Class B
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Example 2 String Classification
Class 1 abnc n0, 1, 2, . ac,
abc, abbc, abbbc, Class 2 anbc n0,
1, 2, . bc, abc, aabc, aaabc,
Given a string of symbols from the set
Sa,b,c determine whether the string comes from
class 1 or class 2 or neither class abbbbbbc -gt
Class 1 aaaabc -gt Class 2 aabbcc -gt
neither class Use Syntactical Approach
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Grammar
A grammar G is a four tuple G (S,T, N, P ) S
a start symbol T a set of terminals N a set of
nonterminals P a set of production rules
For our example abnc n0, 1, 2, .
Ss T c N a,b P s-gta,
a-gtab , b-gtbb, b -gtbc
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Use Neural Networks
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Example 4 Statistical Method
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Basic Pattern Classification Structure
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Decision Rule for Deciding a 0 or a 1
If y1 gt T decide 0 y1 lt T decide 1
y1 T flip an honest coin
decide 0if it comes up heads
decide 1 if it comes up tails
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Lecture 3 Summary
1. Classification Examples 2. Basic Pattern
Classification Structure 3. Statistical Pattern
Classification