CSE  Sargur Srihari  Introduction to Pattern Recognition Sargur N
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CSE Sargur Srihari Introduction to Pattern Recognition Sargur N

Srihari sriharicedarbuffaloedu Dept of Computer Science Engineering State University of New York at Buffalo brPage 2br CSE 555 Sargur Srihari 2 What is a Pattern A pattern is the opposite of chaos it is an entity vaguely defined that could be given

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CSE Sargur Srihari Introduction to Pattern Recognition Sargur N




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Presentation on theme: "CSE Sargur Srihari Introduction to Pattern Recognition Sargur N"— Presentation transcript:


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CSE 555: Sargur Srihari 1 Introduction to Pattern Recognition Sargur N. Srihari srihari@cedar.buffalo.edu Dept. of Computer Science & Engineering State University of New York at Buffalo
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CSE 555: Sargur Srihari 2 What is a Pattern? A pattern is the opposite of chaos; it is an entity vaguely defined, that could be given a name. A pattern is an abstract object, such as a set of measurements describing a physical object.
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CSE 555: Sargur Srihari 3 Examples of Patterns Handwritten Characters Postnet ar Code Fingerprint UPC BarCode Animal Footprint

Data Trend
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CSE 555: Sargur Srihari 4 PR Definitions heory, Algorithms, Systems to put Patterns into Categories lassification of Noisy or Complex Data elate Perceived Pattern to Previously Perceived Patterns
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CSE 555: Sargur Srihari 5 Example Problem: Handwritten Digit Recognition andcrafted rules will result in large no of rules and exceptions etter to have a machine that learns from a large training set Wide variability of same numeral
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CSE 555: Sargur Srihari 6 Role of Machine Learning rincipled way of building high performance information

processing systems M v L has origins in Computer Science R has origins in Engineering hey are different facets of the same field Language Related Technologies R, NLP, DAR, ASR umans perform them well ifficult to specify algorithmically
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CSE 555: Sargur Srihari 7 Machine Learning rogramming computers to use example data or past experience Well-Posed Learning Problems computer program is said to learn from experience E ith respect to class of tasks and performance measure , f its performance at tasks , as measured by , improves with experience
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CSE 555: Sargur

Srihari 8 Classification Process (Decision as opposed to Inference)
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CSE 555: Sargur Srihari 9 Pattern Recognition Applications S ision ace/Handwriting/Hand peech peaker/Speech ouch aptics lfaction pple Ripe? TEXTUAL DATA Text Categorization nformation Retrieval ata Mining ntrusion Detection enome Sequence Matching
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CSE 555: Sargur Srihari 10 Pattern Recognition Processes bjects to be classified are sensed by transducer (camera) ignals are preprocessed eatures are extracted lassification is emitted
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CSE 555: Sargur Srihari 11 Generalization


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CSE 555: Sargur Srihari 12 Pattern Recognition System
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CSE 555: Sargur Srihari 13 Design Cycle
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CSE 555: Sargur Srihari 14 Document Recognition Applications ptical Character Recognition (OCR) andwriting Recognition riter Recognition
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CSE 555: Sargur Srihari 15 Writer Recognition Preprocessing Features Similarity
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CSE 555: Sargur Srihari 16 Address Interpretation Problem Pattern recognition tasks bject recognition (address vs non-address) wo-class discrimination (mp vs hw) ew class recognition (digits) olistic

vs analytical (words) ontextual-hmm(zip codes, words) any classes, but cataloged (postal directory)
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Contextual Information ountry/State/City IP Code treet Name rimary No (Street/PO Box ) econdary No (Apt) irm/Personal Name CSE 555: Sargur Srihari 17