Prologue to Example Acknowledgment.


58 views
Uploaded on:
Category: Animals / Pets
Description
9/1/2011. Copyright, G. A. Tagliarini, PhD. 2. Acknowledgment Or Classification. Acknowledgment Etymologically, the demonstration of reconsidering Involves
Transcripts
Slide 1

Prologue to Pattern Recognition What was that… .?

Slide 2

Recognition Or Classification Recognition Etymologically, the demonstration of reconsidering Involves "recognizing" or "recognizing" Classification Etymologically, the demonstration of isolating into gatherings Involves "sorting" as indicated by what a thing is called or "partner" to a gathering Copyright, G. A. Tagliarini, PhD

Slide 3

Recognition Rigel (900 ly) Betelgeuse (300 ly) Copyright, G. A. Tagliarini, PhD

Slide 4

Input source Sensing Segmentation Feature Extraction Recognition The Classification Process System Response Copyright, G. A. Tagliarini, PhD

Slide 5

Sensing Depends on the application area Consistency can change generally inside and crosswise over areas Must result in a reason for measuring prejudicial elements—recognizing attributes must be "discernible" Copyright, G. A. Tagliarini, PhD

Slide 6

Segmentation: Extremely Challenging A required preprocessing step Examples: What is the reason for isolating parts of a picture? Shading, closeness, limit shapes, "surface" Where are the limits between manually written letters or words? At the point when does a talked word begin/stop? Copyright, G. A. Tagliarini, PhD

Slide 7

Feature Extraction What elements are striking for the order? Are the components powerful? Do they change with parameters, for example, time, recurrence, scale, interpretation, pivot, or closeness? Do subsets of the elements give grouping adequacy? Copyright, G. A. Tagliarini, PhD

Slide 8

Classification What are the classifier outline destinations? Minimize order error(s) Type 1 (dismiss a genuine Ho) Type 2 (neglect to dismiss a false Ho) Generalization Reduced computational intricacy Reduced algorithmic many-sided quality Noise Copyright, G. A. Tagliarini, PhD

Slide 9

System Response So what? Copyright, G. A. Tagliarini, PhD

Slide 10

Machine Learning: Creating a Classifier Adaptively Supervised learning Feedforward system and backpropagation Hopfield Unsupervised learning ART Kohonen Copyright, G. A. Tagliarini, PhD

Slide 11

Some Sample Problems Intrusion identification in system activity Handwritten character/word acknowledgment Speech acknowledgment Sonar acoustic transient acknowledgment Face acknowledgment Fingerprint order Copyright, G. A. Tagliarini, PhD

Slide 12

Key Questions What are the cases? (information) What attributes recognize the class models? (highlights) How will unfair confirmation be consolidated to settle on a choice? (classifier) How well does it work? (appraisal) Copyright, G. A. Tagliarini, PhD

Slide 13

Classifier Construction Data accumulation or era Data may not be bottomless or accessible Identify highlights Determines preprocessing necessities Choose a classifier to execute Model may recommend the classifier Model may require versatile development (preparing) Performance Assessment Copyright, G. A. Tagliarini, PhD

Slide 14

No Free Lunch Theorem Loosely expressed, "There is no classifier display that will be ideal for all characterization issues." Copyright, G. A. Tagliarini, PhD

Recommended
View more...