Prof. Corso, [email protected], SUNY Buffalo
Prepared by David Johnson
CSE 455/555 Spring 2013 Quiz 2 of 14
Solutions
Problem 1: Recall (2pts) (Answer in one sentence only.)
Name one reason one might use a decision tree or forest rather than another type
Prof. Corso, [email protected], SUNY Buffalo
Prepared by David Johnson
CSE 455/555 Spring 2013 Quiz 1 of 14
Solutions
Problem 1: Recall (2pts) (Answer in one sentence only.)
What is a decision boundary?
A decision boundary is the region of a problem spac
CSE 455/555 Spring 2013 Quiz 13 of 14
Jason J. Corso, [email protected], SUNY Buffalo
Solution by Yingbo Zhou
Name:
ID#:
Section:
455
or
2
555
8
10
Directions The quiz is closed book/notes. You have 10 minutes to complete it; use this paper only.
Problem
Jason J. Corso, [email protected], SUNY Buffalo
Solutions by David Johnson
CSE 455/555 Spring 2013 Quiz 4 of 14
Solutions
All problems are worth 2 points.
1. What is one real-world application that last weeks guest lecturers pattern recognition technolog
Jason J. Corso, [email protected], SUNY Buffalo
Solutions by David Johnson
CSE 455/555 Spring 2013 Quiz 11 of 14
Solutions
Problem 1: Recall (2pts) (Answer in one sentence only.)
What is the objective criterion of spectral clustering (i.e. what value is
Jason J. Corso, [email protected], SUNY Buffalo
Solutions by David Johnson
CSE 455/555 Spring 2013 Quiz 6 of 14
Solutions
Problem 1: Recall (2pts) (Answer in one sentence only.)
What quantity is PCA maximizing during dimension reduction?
PCA maximizes th
CSE 455/555 Spring 2013 Quiz 12 of 14
Jason J. Corso, [email protected], SUNY Buffalo
Solutions by Yingbo Zhou
Name:
ID#:
Section:
455
or
555
2
8
10
Directions The quiz is closed book/notes. You have 10 minutes to complete it; use this paper only.
Proble
Jason J. Corso, [email protected], SUNY Buffalo
Solutions by Yingbo Zhou
CSE 455/555 Spring 2013 Quiz 9 of 14
Name:
ID#:
Section:
455
or
555
2
2
2
2
2
10
Directions The quiz is closed book/notes. You have 10 minutes to complete it; use this paper only.
A
Jason J. Corso, [email protected], SUNY Buffalo
Solutions by Yingbo Zhou
CSE 455/555 Spring 2013 Quiz 7 of 14
Name:
ID#:
Section:
455
or
2
555
8
10
Directions The quiz is closed book/notes. You have 10 minutes to complete it; use this paper only.
Problem
Jason J. Corso, [email protected], SUNY Buffalo
Solutions by David Johnson
CSE 455/555 Spring 2013 Quiz 6 of 14
Solutions
Problem 1: Recall (2pts) (Answer in one sentence only.)
What is a support vector?
A support vector is a point that lies (approximate
Jason J. Corso, [email protected], SUNY Buffalo
Solutions by Yingbo Zhou
CSE 455/555 Spring 2013 Quiz 8 of 14
Name:
ID#:
Section:
455
or
2
555
8
10
Directions The quiz is closed book/notes. You have 10 minutes to complete it; use this paper only.
Problem
Jason J. Corso, [email protected], SUNY Buffalo
Solutions by David Johnson
CSE 455/555 Spring 2013 Quiz 5 of 14
Solutions
Problem 1: Recall (2pts) (Answer in one sentence only.)
Given an unbiased linear discriminant dened by the augmented weight vector a
Solutions provided by David Johnson. See
Code for the solutions to 1-3.
4. SV lists the coordinates of the identied support vectors (for those whove forgotten, the support vectors are
the points nearest the decision boundaryand by extension are the points
Spectral Clustering
Braunhofer Matthias (matthias.braunhofer [at] gmail.com)
Strumpohner Juri (juri.strumpohner [at] gmail.com)
Data Warehousing and Data Mining
Free University of Bozen-Bolzano
January 23, 2009
Abstract
Clustering is a popular data mining
Spectral Clustering
Jing Gao
SUNY Buffalo
1
Motivation
Complex cluster shapes
K-means performs poorly because it can only find spherical
clusters
Spectral approach
Use similarity graphs to encode local neighborhood information
Data points are vertice
Primer
2008 Nature Publishing Group http:/www.nature.com/naturebiotechnology
What is the expectation maximization algorithm?
Chuong B Do & Serafim Batzoglou
The expectation maximization algorithm arises in many computational biology applications that inv
Bayesian Decision Theory
Robert Jacobs Department of Brain & Cognitive Sciences University of Rochester
Types of Decisions
Many different types of decision-making situations Single decisions under uncertainty
Ex: Is a visual object an apple or an orange
The Expectation Maximization Algorithm
Frank Dellaert College of Computing, Georgia Institute of Technology Technical Report number GIT-GVU-02-20 February 2002
Abstract This note represents my attempt at explaining the EM algorithm (Hartley, 1958; Dempste
Getting Started
with Matlab
CSE474 Machine Learning
2012 Fall
TA: Yu Liu
[email protected]
Matlab Introduction
Matlab is a program for doing numerical
computation. It was originally designed for
solving linear algebra type problems using
matrices. Its nam
Structure Learning in Bayesian Networks
(mostly Chow-Liu)
Sue Ann Hong
11/15/2007
Chow-Liu
Goal: nd a tree that maximizes the data likelihood
Algorithm
Compute weight I(Xi,Xj) of each (possible) edge (Xi,Xj)
Find a maximum weight spanning tree (MST)
G
CSE 555 Spring 2009 Final Exam
Jason J. Corso
Computer Science and Engineering
University at Buffalo SUNY
[email protected]
Thursday 7 May 2009, 11:45 AM - 02:45 PM, KNOX 14
Brevity is the soul of wit.
-Shakespeare
The exam is worth 100 points total
CSE 555 Spring 2010 Final Exam
Jason J. Corso
Computer Science and Engineering
SUNY at Buffalo
[email protected]
Friday 30 April 2010, 8:00 - 11:00, Norton 218
Brevity is the soul of wit.
-Shakespeare
There are 6 questions each worth 20pts; choose 5 of t
CSE 455/555 Spring 2011 Final Exam
Jason J. Corso
Computer Science and Engineering
SUNY at Buffalo
[email protected]
Date 10 May 2011
Brevity is the soul of wit.
-Shakespeare
Name:
Nickname:
Section:
25
455
or
25
25
25
25
100
555
Nickname is a unique ide
CSE 455/555 Spring 2012 Mid-Term Exam
Brevity is the soul of wit.
-Shakespeare
Jason J. Corso, [email protected]
Computer Science and Engineering, SUNY at Buffalo
Date 18 Mar 2012
Name:
Nickname:
Section:
25
455
or
25
25
25
100
555
Nickname is a unique i
CSE 455/555 Spring 2012 Final Exam
Jason J. Corso, [email protected]
Computer Science and Engineering, SUNY at Buffalo
Date 3 May 2012, 11:45 - 14:45
Location Knox 04
Brevity is the soul of wit.
-Shakespeare
Name:
Nickname:
Section:
5
455
or
20
20
20
20
CSE 455/555 Introduction to Pattern Recognition
SUNY at Buffalo
Syllabus for Spring 2013
Last updated: 3 Jan 2013
Instructor:
Jason Corso (UBIT: jcorso)
Course Webpage: http:/www.cse.buffalo.edu/jcorso/t/CSE555 or
http:/www.cse.buffalo.edu/jcorso/t/CSE455
Boosting and AdaBoost
Jason Corso
SUNY at Bualo
J. Corso (SUNY at Bualo)
Boosting and AdaBoost
1 / 62
Introduction
Weve talked loosely about
1
2
Lack of inherent superiority of any one particular classier; and
Some systematic ways for selecting a particul
L7: Kernel density estimation
Non-parametric density estimation
Histograms
Parzen windows
Smooth kernels
Product kernel density estimation
The nave Bayes classifier
CSCE 666 Pattern Analysis | Ricardo Gutierrez-Osuna | [email protected]
1
Non-parametric density es