intro - Pattern Recognition Artificial Neural Networks, and...

Info iconThis preview shows pages 1–21. Sign up to view the full content.

View Full Document Right Arrow Icon
Pattern Recognition Pattern Recognition Artificial Neural Networks, Artificial Neural Networks, and Machine Learning and Machine Learning Yuan-Fang Wang Department of Computer Science University of California Santa Barbara, CA 93106, USA
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2 What is a Pattern?
Background image of page 2
3
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
4
Background image of page 4
5
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
6 x DNA patterns b AGCTCGAT x Protein Patterns b 20 amino acids
Background image of page 6
7
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
8
Background image of page 8
9
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
10 Faces Finger prints
Background image of page 10
11 Other Patterns x Insurance, credit card applications b applicants are characterized by a pattern h # of accidents, make of car, year of model h income, # of dependents, credit worthiness, mortgage amount x Dating services b Age, hobbies, income, etc. establish your “desirability”
Background image of page 11

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
12 Other Patterns x Web documents b Key words based description (e.g., documents containing War, Bagdad, Hussen are different from those containing football, NFL, AFL, draft, quarterbacks) x Intrusion detection b Usage and connection patterns x Cancer detection b Image features for tumors, patient age, treatment option, etc.
Background image of page 12
13 Other Patterns x Housing market b Location, size, year, school district x University ranking b Student population, student-faculty ratio, scholarship opportunities, location, faculty research grants, etc. x Too many b E.g., http://www.ics.uci.edu/~mlearn/MLSummary.html
Background image of page 13

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
14 What is a pattern? x A pattern is a set of objects, processes or events which consist of both deterministic and stochastic components x A pattern is a record of certain dynamic processes influenced both by deterministic and stochastic factors
Background image of page 14
15 What is a Pattern? (cont.) Completely regular, deterministic (e.g., crystal structure) Completely random (e.g., white noise) Constellation patterns, texture patterns, EKG patterns, etc.
Background image of page 15

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
16 x Classifies “patterns” into “classes” x Patterns (x) b have “measurements” or “features” x Classes ( ) b likelihood (a prior probability ) b class-conditional density x Classifier (f(x) -> ) x An example b four coin classes: penny, nickel, dime, and quarter b measurements: weight, color, size, etc. b Assign a coin to a class based on its size, weight, etc. P i ( ) ϖ i p x i ( | ) What is Pattern Recognition? i We use P to denote probability mass function ( discrete ) and p to denote probability density function ( continuous )
Background image of page 16
17 An Example
Background image of page 17

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
18 Another Example
Background image of page 18
19 Features x The intrinsic traits or characteristics that tell one pattern (object) apart from another x Features extraction and representation allows b Focus on relevant, distinguishing parts of a pattern b Data reduction and abstraction
Background image of page 19

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
20 Feature Selection x More an art than a science x Effectiveness criteria: size population Size alone is not effective
Background image of page 20
Image of page 21
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 56

intro - Pattern Recognition Artificial Neural Networks, and...

This preview shows document pages 1 - 21. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online