Final project questions Let's take up to an hour to Review progress Answer questions Referencing sources in the term project Direct quotes -- Place in quotes or indent and cite source in footnote or reference Extensive paraphrase--Cite source at beginning of chapter or section and explain degree to which it was used in a footnote Common knowledge -- No reference reqd mit 人 16881
Final Project Questions • Let’s take up to an hour to – Review progress – Answer questions • Referencing sources in the term project – Direct quotes -- Place in quotes or indent and cite source in footnote or reference – Extensive paraphrase -- Cite source at beginning of chapter or section and explain degree to which it was used in a footnote – Common knowledge -- No reference req’d 16.881 MIT
Mahalanobis taguchi System Design of systems which rely on Accurate Classification ■■■■ mit 人 16881
Mahalanobis Taguchi System Design of Systems which Rely on Accurate Classification = ? 16.881 MIT
Outline Review classification problems Introduce the mahalanobis distance Demo on character recognition Mahalanobis Taguchi System(MTS) Case study on fire alarm system mit 人 16881
Outline • Review classification problems • Introduce the Mahalanobis distance • Demo on character recognition • Mahalanobis Taguchi System (MTS) • Case study on fire alarm system 16.881 MIT
Classification problems Many systems function by classifying instances into classes Character recognition Does belong to A B. C.? Fire detection Does this amount of smoke and heat indicate a fire or a BBo? Air bag deployment Do these accelerometer inputs indicate a crash, a bumpy road, a hard stop? http://www-engr.sjsu.edu/knapp/hcirodpr/prhome.htm Pattern Recognition for HCl, RichardO duda o16 B81 Department of Electrical Engineering, San Jose State University MIT
Classification Problems • Many systems function by classifying instances into classes – Character recognition • D o es R belong to A, B, C, ...? – Fire detection • Does this amount of smoke and heat indicate a fire or a BBQ? – Air bag deployment • Do these accelerometer inputs indicate a crash, a bumpy road, a hard stop? http://www-engr. sjsu.edu/~knapp/ HCIRODPR/PR_home.htm Pattern Recognition for HCI, Richard O. Duda 16.881 Department of Electrical Engineering, San Jose State University MIT
Design lssues in Classifier Systems What should be measured? How should measurements be processed? What is the criterion for demarcation? What are the consequences of error? Classified instance as a. but it isn't A Classified instance as not a. but it is mit 人 16881
Design Issues in Classifier Systems • What should be measured? • How should measurements be processed? • What is the criterion for demarcation? • What are the consequences of error? – Classified instance as A, but it isn’t A. – Classified instance as not A, but it is. 16.881 MIT
Features Classification is made on the basis of measured features Features should Be easy (or inexpensive ) to measure or extract Clearly demarcate classes 上 Xamples Medical d agnosis Character recognition □■ DISPLAY CIin) mit 人 16881
Features • Classification is made on the basis of measured features • Features should – Be easy (or inexpensive) to measure or extract – Clearly demarcate classes • Examples – Medical diagnosis – Character recognition DISPLAY( Clin) DISPLAY( Dlin) 16.881 MIT
Feature Vectors Generally, there are several features required to make a classification These features x can be assembled into a vector Any object to be classified is represented by a point in n dimensional feature space mit 人 16881
Feature Vectors • Generally, there are several features required to make a classification • These features xi can be assembled into a vector • Any object to be classified is represented by a point in n dimensional feature space x1 x = x2 x3 x1 x2 x3 x 16.881 MIT
Joint gaussian distribution Density function entirely determined by mean vector and correlation matrix m Curves of constant probabi lty are ellispolds X exp3-(x-m)K(x-m) 2丌 mit 人 16881
Joint Gaussian Distribution • Density function entirely determined by mean vector and correlation matrix • Curves of constant m x2 x1 probability are ellispoids p ( x) = ( 1 ) exp − 1 (x − m )T K −1 (x − m ) 2 K 2 m π 16.881 MIT
Pattern Recognition Model There are two major elements required for pattern recognition a feature extractor A classifier x Raw data Feature Categor extractor Classifier n mit 人 16881
Pattern Recognition Model • There are two major elements required for pattern recognition – A feature extractor – A classifier Raw data Feature Category extractor Classifier x1 x2 xn 16.881 MIT
Template matching Define a template for each class · Choose class based on Maximum correlation or Minimum error What are the limitations? 人 16881 DISPLAY DIin) DISPLAY DIin) mit
Template matching • Define a “template” for each class • Choose class based on – Maximum correlation or – Minimum error • What are the limitations? 16.881 DISPLAY(Dlin) DISPLAY(Dlin) MIT
