H5 - Statistical Pattern Recognition University of Maryland...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Statistical Pattern Recognition University of Maryland, College Park October 8, 2009 Professor Rama Chellappa Handout 5 Homework 5 This problem set is due Thursday October 15 at 11:00AM . 1. Consider a case in which class ω 1 consists of the two feature vectors [0 , 0] T and [0 , 1] T and class ω 2 of [1 , 0] T and [1 , 1] T . Use the perceptron in it’s reward and punishment form (referred to as the fixed increment single sample perceptron (FISSP) in class), with ρ = 1 (referred to as learning rate η in class) and w (0) = [0 , 0] T , to design the line separating the two classes. 2. Generate 50 feature vectors for each of the two classes p ( x | ω 1 ) ∼ N parenleftbiggparenleftbigg 1 1 parenrightbigg ,σ 2 I parenrightbigg , p ( x | ω 2 ) ∼ N parenleftbiggparenleftbigg parenrightbigg ,σ 2 I parenrightbigg , σ 2 = 0 . 2. To guarantee linear separability of the classes disregard vectors with x 1 + x 2 < 1 for class ω 1 and vectors with x 1 + x 2 > 1 for class ω 2 ....
View Full Document

This note was uploaded on 10/26/2009 for the course CMSC 828 taught by Professor Staff during the Fall '05 term at Maryland.

Ask a homework question - tutors are online