Support Vector Machine and Implementation using Weka

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This has a detailed explanation of Machine Learning algorithm and it's implementation using Iris dataset using Weka.

Ingénierie

Friendly Introduction to Machine Learning
Learn from experience Learn from data Follow Instructions

What is Machine Learning?
Machine Learning is the science of getting computers to act without being
explicitly programmed.
Computer
Data
Program
Output
Computer
Data
Output
Program
Traditional Programming:
Machine Learning:

Why is it trending now?
 Availability of many different kinds of data.
 Greater computational power.
 Exponential decrease in the price of powerful computing
resources.
 Train computers to do things that are very difficult to
program.

Machine Learning Methods
ML Methods
Supervised
Learning
Unsupervised
Learning
ClassificationRegression Clustering Association

Supervised Learning : Regression
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2 2000 K.R.Puram 25000
3 2000 Banashankari 20000
1 850 K.R.Puram 15000
3 2500 K.R.Puram 35000
4 3000 Whitefield 40000
2 1500 K.R.Puram ?

Supervised Learning : Classification
Sepal Length Sepal Width Petal Length Petal Width Class
5.1 3.5 1.4 0.2 Iris - Setosa
4.9 3.0 1.4 0.2 Iris - Setosa
7.0 3.2 4.7 1.4 Iris - Versicolor
6.4 3.2 4.5 1.5 Iris - Versicolor
6.4 3.2 5.3 2.3 Iris - Verginica
7.9 3.8 6.4 2.0 Iris - Verginica

Unsupervised Learning
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2 2000 K.R.Puram
3 2000 Banashankari
1 850 K.R.Puram
3 2500 K.R.Puram
4 3000 Whitefield

Training Data
Machine Learning
Algorithm
ClassifierTest Data Prediction

Support Vector Machine
 Vladimir Vapnik laid most of the groundwork for SVM
while working on his PhD thesis in the Soviet Union in
1960s.
 A supervised Machine Learning algorithm
 Used for both classification and regression
 It’s a binary classifier

Support Vector Machine
.
SVM is a classifier method that performs classification tasks by constructing
hyperplanes.

Identifying the right Hyperplane?
Margin
Maximum perpendicular distance between the nearest data point and hyperplane -
Margin

How to compute MARGIN?
 Consider w perpendicular to median.
 Consider u, which we would like to classify.
 Decision rule:
w • u ≥ c ⇒ u is +, or
w • u + b ≥ 0 ⇒ u is +,
where c= -b.
 w • x++ b ≥ 1 (+ve sample)
w • x-+ b ≤ −1 (-ve sample)

 Introduce yi = 1 for + data and yi = -1 for -
data.
yi(x • w + b) ≥ 1, or
yi(x • w + b) − 1 ≥ 0,
 Margin =
How to compute MARGIN?

Maximizing the MARGIN
 Margin = 2||w||-1
 maximizing 2||w||-1 = maximizing ||w||-1
= minimizing ||w||
= minimizing
 Using Lagrange multipliers

 w • x++ b ≥ 1 (+ve sample)
w • x-+ b ≤ −1 (-ve sample)
 Decision Rule:

What if the data is not linearly separable?
Idea : Separable in higher dimension
X
Y
Z=X2 +Y2
X
Z
Y

Where does SVM get its name?
 Separating plane is usually determined by only a handful
of data points.
 The points that help determine the hyperplane are called
Support Vectors.
 The hyperplane itself is a classifying machine.

WEKA: A Machine
Learning tool
 Waikato Environment for Knowledge
Analysis
 Open source software tool
 Developed at The University of
Waikato
 Collection of visualization tools and
algorithms

IRIS Dataset
Attributes:
1. Sepal Length
2. Sepal Width
3. Petal Length
4. Petal Width

References:
 https://www.youtube.com/watch?v=IpGxLWOIZy4
 https://weka.waikato.ac.nz/dataminingwithweka
 https://www.analyticsvidhya.com
 https://medium.com/@ageitgey
 https://www.youtube.com/watch?v=_PwhiWxHK8o&t=99s
 http://www.svm-tutorial.com
 https://www.youtube.com/watch?v=ZDfVal_4HMA
 http://www.eric-kim.net/eric-kim-net/posts/1/kernel_trick.html
 http://www4.stat.ncsu.edu/~post/todd/SVMslides.pdf

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Support Vector Machine and Implementation using Weka

1. SVM, A Machine Learning Algorithm

2. Friendly Introduction to Machine Learning Learn from experience Learn from data Follow Instructions

3. What is Machine Learning? Machine Learning is the science of getting computers to act without being explicitly programmed. Computer Data Program Output Computer Data Output Program Traditional Programming: Machine Learning:

4. Why is it trending now?  Availability of many different kinds of data.  Greater computational power.  Exponential decrease in the price of powerful computing resources.  Train computers to do things that are very difficult to program.

5. Machine Learning Methods ML Methods Supervised Learning Unsupervised Learning ClassificationRegression Clustering Association

6. Supervised Learning : Regression Bedrooms Sq. feet Neighbourhood Rent 2 2000 K.R.Puram 25000 3 2000 Banashankari 20000 1 850 K.R.Puram 15000 3 2500 K.R.Puram 35000 4 3000 Whitefield 40000 2 1500 K.R.Puram ?

7. Supervised Learning : Classification Sepal Length Sepal Width Petal Length Petal Width Class 5.1 3.5 1.4 0.2 Iris - Setosa 4.9 3.0 1.4 0.2 Iris - Setosa 7.0 3.2 4.7 1.4 Iris - Versicolor 6.4 3.2 4.5 1.5 Iris - Versicolor 6.4 3.2 5.3 2.3 Iris - Verginica 7.9 3.8 6.4 2.0 Iris - Verginica

8. Unsupervised Learning Bedrooms Sq. feet Neighbourhood 2 2000 K.R.Puram 3 2000 Banashankari 1 850 K.R.Puram 3 2500 K.R.Puram 4 3000 Whitefield

9. Data

10. Training Data Machine Learning Algorithm ClassifierTest Data Prediction

11. Support Vector Machine  Vladimir Vapnik laid most of the groundwork for SVM while working on his PhD thesis in the Soviet Union in 1960s.  A supervised Machine Learning algorithm  Used for both classification and regression  It’s a binary classifier

12. Support Vector Machine . SVM is a classifier method that performs classification tasks by constructing hyperplanes.

13. Identifying the right hyperplane?

14. Identifying the right Hyperplane? Margin Maximum perpendicular distance between the nearest data point and hyperplane - Margin

15. How to compute MARGIN?  Consider w perpendicular to median.  Consider u, which we would like to classify.  Decision rule: w • u ≥ c ⇒ u is +, or w • u + b ≥ 0 ⇒ u is +, where c= -b.  w • x++ b ≥ 1 (+ve sample) w • x-+ b ≤ −1 (-ve sample)

16.  Introduce yi = 1 for + data and yi = -1 for - data. yi(x • w + b) ≥ 1, or yi(x • w + b) − 1 ≥ 0,  Margin = How to compute MARGIN?

17. Maximizing the MARGIN  Margin = 2||w||-1  maximizing 2||w||-1 = maximizing ||w||-1 = minimizing ||w|| = minimizing  Using Lagrange multipliers

18.  Using Lagrange multipliers

19.  w • x++ b ≥ 1 (+ve sample) w • x-+ b ≤ −1 (-ve sample)  Decision Rule:

20.

21. Types of Data: Linearly Separable

22. Non - Linearly Separable Data

23.

24. What if the data is not linearly separable? Idea : Separable in higher dimension X Y Z=X2 +Y2 X Z Y

25. Where does SVM get its name?  Separating plane is usually determined by only a handful of data points.  The points that help determine the hyperplane are called Support Vectors.  The hyperplane itself is a classifying machine.

26. WEKA: A Machine Learning tool  Waikato Environment for Knowledge Analysis  Open source software tool  Developed at The University of Waikato  Collection of visualization tools and algorithms

27.

28.

29.

30.

31. IRIS Dataset Attributes: 1. Sepal Length 2. Sepal Width 3. Petal Length 4. Petal Width

32. IRIS Flowers

33.

34. Implementation of SVM in WEKA

35.

36. References:  https://www.youtube.com/watch?v=IpGxLWOIZy4  https://weka.waikato.ac.nz/dataminingwithweka  https://www.analyticsvidhya.com  https://medium.com/@ageitgey  https://www.youtube.com/watch?v=_PwhiWxHK8o&t=99s  http://www.svm-tutorial.com  https://www.youtube.com/watch?v=ZDfVal_4HMA  http://www.eric-kim.net/eric-kim-net/posts/1/kernel_trick.html  http://www4.stat.ncsu.edu/~post/todd/SVMslides.pdf

Support Vector Machine and Implementation using Weka

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