ICML 2009 Tutorial Survey of Boosting from an Optimization Perspective Part I: Entropy Regularized LPBoost Part II: Boosting from an Optimization Perspective
Manfred K. Warmuth - UCSC S.V.N. Vishwanathan - Purdue & Microsoft Research
Updated: March 23, 20
5 Optimization
Optimization plays an increasingly important role in machine learning. For instance, many machine learning algorithms minimize a regularized risk functional: min J(f ) := (f ) + Remp (f )
f
(5.1)
with the empirical risk Remp (f ) := 1 l(f (
Probability distribution
From Wikipedia, the free encyclopedia
Jump to: navigation, search This article is about probability distribution. For generalized functions in mathematical analysis, see Distribution (mathematics). For other uses, see Distribution
CS 580 and STAT 598A: Project Proposal
Due: 23rd March 2010
The goal of the course project is implement and investigate the behavior of a statistical technique that interests you and to use it to analyze some nontrivial datasets (at least 10,000 data poin
normalize_mtrx <- function( ip_mat, row ) cfw_
# Normalizes rows to add up to one if row = TRUE
# Else normalizes columns
if(row) cfw_
# We want the rows to add up to one
rslt <- ip_mat / rowSums(ip_mat)
else cfw_
# We want the columns to add up to one
r
mean;
ifelse
mean
mean(;
mean(
c(1,2,3)
x <- print("Hello")
x
x <- cfw_ print("Hello"); 5
x
if("True") cfw_ print("Hello")
isTRUE("TRUE")
isTRUE(as.logical("TRUE")
isTRUE(as.logical("TRUTH")
isTRUE(as.logical("YES")
p <- 0
log(p)
p * log(p)
p <- exp(-c(1
6 Conditional Densities
A number of machine learning algorithms can be derived by using conditional exponential families of distribution (Section 2.3). Assume that the training set cfw_(x1 y1 ) . . . (xm ym ) was drawn iid from some underlying distributio
6 Linear Models
A hyperplane in a space H endowed with a dot product is described by the set cfw_x H| w x + b = 0 (6.1)
where w H and b R. Such a hyperplane naturally divides H into two half-spaces: cfw_x H| w x + b 0 and cfw_x H| w x + b < 0, and hence c
2 Density Estimation
2.1 Limit Theorems Assume you are a gambler and go to a casino to play a game of dice. As it happens, it is your unlucky day and among the 100 times you toss the dice, you only see '6' eleven times. For a fair dice we know that each f
Emacs Quick Reference
Key Bindings Compiling Debugging Controlling Windows Emacs Manual For more information, see Chap. 23 in H. Hahn, Harley Hahn's Student Guide to UNIX, 2nd edition, McGraw-Hill, 1996; Appendix F summarizes most of the emacs commands. F
CS 598 and STAT 598A: Homework 1
Due: 9th February 2010
1. Attempt as many problems as possible 2. No points for random guessing. You have to explain your answers. 3. Mail your source code to [email protected] before the class on 9th of February 2010.
CS 598 and STAT 598A: Homework 2
Due: 2nd March 2010
1. Attempt as many problems as possible 2. No points for random guessing. You have to explain your answers. 3. Mail your source code to [email protected] before the class on 2nd of March 2010. You m
CS 598 and STAT 598A: Homework 3
Due: 23rd March 2010
1. Attempt as many problems as possible 2. No points for random guessing. You have to explain your answers. 3. Mail your source code to [email protected] before the class on 23rd of March 2010. You
CS 598 and STAT 598A: Homework 4
Due: 6th April 2010
1. Attempt as many problems as possible 2. No points for random guessing. You have to explain your answers. 3. Mail your source code to [email protected] before the class on 6th of April 2010. You m
CS 598 and STAT 598A: Homework 5
Due: 20th April 2010
1. Attempt as many problems as possible 2. No points for random guessing. You have to explain your answers. 3. Mail your source code to [email protected] before the class on 20th of April 2010. You
Introduction to Machine Learning
CS 590 and STAT 598A, Spring 2010
Instructor: S.V. N. Vishwanathan (email: vishy)
http:/www.stat.purdue.edu/~vishy/introml/introml.html
January 12, 2010
S.V N. Vishwanathan (Purdue University) .
Introduction to Machine Lea