Linear Regression and Support Vector Regression
Linear Regression and Support Vector Regression
Paul Paisitkriangkrai paulp@cs.adelaide.edu.au The University of Adelaide
24 October 2012
Outlines
? Regression overview ? Linear regression ? Support vector regression ? Machine learning tools available
Regression Overview
CLUSTERING
CLASSIFICATION
REGRESSION (THIS TALK)
+ + ++ ++ +
+
+ ++
+ ++ + +
+
+ +
+++
--
-
--
+
++ + +++ +
K-means
? Decision tree ? Linear Discriminant Analysis ? Neural Networks ? Support Vector Machines ? Boosting
? Linear Regression ? Support Vector Regression
Group data based on their characteristics
Separate data based on their Find a model that can explain
labels
the output given the input
Data processing flowchart (Income
prediction)
Raw
Processed
data
data
Transformed +
data
++
+ +++ +
Pre-processing (noise/outlier removal)
Feature extraction and selection
Regression
Sex Age Hei Income ght
M 20 1.7 25,000
F 30 1.6 55,000
M
1.8 30,000
...
Height and sex seem to be irrelevant.
Mis-entry (should have been 25!!!)
Income
+ ++ ++ ++ +
Age
Linear Regression
? Given data with n dimensional variables and 1 target-variable (real number) {(x1, y1),(x2, y2 ),...,(xm, ym )}
Where x n, y
? The objective: Find a function f that returns the best fit. f : n
? Assume that the relationship between X and y is approximately linear. The model can be represented as (w represents coefficients and b is an intercept)
f (w1,..., wn,b) y w x b
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