Support vector-machines-presentation

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The document discusses support vector machines (SVM), a supervised machine learning algorithm. SVM can be used for both classification and regression problems by finding a hyperplane that separates classes with the maximum margin. It explains key SVM concepts like support vectors, kernels, hyperparameters like gamma and C, and evaluation techniques like cross-validation. Applications mentioned include text categorization, bioinformatics, face recognition and image classification.

MCS 7103: Machine Learning
Simon Alex and Nambaale
Support Vector Machines
Simon Alex and Nambaale MCS 7101 October 8, 2019 1 / 28

Overview
1 Support Vector Machines
Applications of SVM
What is Machine Learning?
What Is SVM?
Features of SVM
How Does SVM Work?
Non-Linear SVM
SVM Use Case
Simon Alex and Nambaale MCS 7101 October 8, 2019 2 / 28

Applications of Support Vector Machines
Text Categorization Bioinformatics Face Recognition Image Classiﬁcation
Simon Alex and Nambaale MCS 7101 October 8, 2019 3 / 28

What is Machine Learning?
Simon Alex and Nambaale MCS 7101 October 8, 2019 4 / 28

Supervised Learning
Simon Alex and Nambaale MCS 7101 October 8, 2019 5 / 28

Unsupervised Learning
Simon Alex and Nambaale MCS 7101 October 8, 2019 6 / 28

Type of Problems in Machine Learning
Simon Alex and Nambaale MCS 7101 October 8, 2019 7 / 28

Where are Support Vector Machines?
Simon Alex and Nambaale MCS 7101 October 8, 2019 8 / 28

What is Support Vector Machine?
Support Vector Machine (SVM) is a supervised learning method used
for classiﬁcation and regression.
SVM separates data using a hyperplane which acts like a decision
boundary between the various classes.
SVM works well for classifying higher-dimensional data (lots of fea-
tures).
Simon Alex and Nambaale MCS 7101 October 8, 2019 9 / 28

Features of Support Vector Machine
SVM is a supervised learning algorithm.
It can be used for both classiﬁcation and regression problems.
SVM can be used for classifying non-linear data using the kernel trick.
Simon Alex and Nambaale MCS 7101 October 8, 2019 10 / 28

How Does SVM Work?
Simon Alex and Nambaale MCS 7101 October 8, 2019 11 / 28

How Does SVM Work?
Simon Alex and Nambaale MCS 7101 October 8, 2019 12 / 28

What is a Support Vector in SVM
Simon Alex and Nambaale MCS 7101 October 8, 2019 13 / 28

What is a Support Vector in SVM
Simon Alex and Nambaale MCS 7101 October 8, 2019 14 / 28

What is a Support Vector in SVM
Simon Alex and Nambaale MCS 7101 October 8, 2019 15 / 28

Non-Linear Support Vector Machine
Simon Alex and Nambaale MCS 7101 October 8, 2019 16 / 28

Non-Linear Support Vector Machine
Simon Alex and Nambaale MCS 7101 October 8, 2019 17 / 28

Deﬁning the Separating Hyperplane
Form of equation deﬁning the decision surface separating the classes is
a hyperplane of the form:
wT
x + b = 0
Where:
w is a weight vector
x is an input vector
b is bias
Simon Alex and Nambaale MCS 7101 October 8, 2019 18 / 28

Deﬁning the Separating Hyperplane
Simon Alex and Nambaale MCS 7101 October 8, 2019 19 / 28

Support Vector Classiﬁcation
In practice, SVM uses SVC to classify data.
Diﬀerent kernels can be used with SVC.
These kernels include: linear, RBF and polynomial.
Some kernels work better than others for a given dataset.
Simon Alex and Nambaale MCS 7101 October 8, 2019 20 / 28

Gamma and C
Gamma controls the shape of the ’peaks’ where the points are raised.
The gamma parameter deﬁnes the degree of non-linearity, with low val-
ues tending high linearity and high values leading to high non-linearity.
Simon Alex and Nambaale MCS 7101 October 8, 2019 21 / 28

Inﬂuence of Gamma on Training Data
Simon Alex and Nambaale MCS 7101 October 8, 2019 22 / 28

Gamma and C
C controls the cost of misclassiﬁcation on the training data.
A high C tries to minimize the misclassiﬁcation of training data leading
to overﬁtting∗ and a low value tries to maintain a smooth classiﬁca-
tion.
∗
Underﬁtting is where the model neither performs well in training nor testing.
Simon Alex and Nambaale MCS 7101 October 8, 2019 23 / 28

Inﬂuence of C on Training Data
Simon Alex and Nambaale MCS 7101 October 8, 2019 24 / 28

Cross-Validation
Cross-Validation is a statistical method of evaluating and comparing
learning algorithms by dividing data into two segments: one used to
learn or train a model and the other used to validate the model.
The basic form of cross-validation is k-fold cross-validation.
Simon Alex and Nambaale MCS 7101 October 8, 2019 25 / 28

K-Fold Cross Validation
Simon Alex and Nambaale MCS 7101 October 8, 2019 26 / 28

Questions?
Simon Alex and Nambaale MCS 7101 October 8, 2019 27 / 28

SVM Use Case
Simon Alex and Nambaale MCS 7101 October 8, 2019 28 / 28

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Support vector-machines-presentation

1. MCS 7103: Machine Learning Simon Alex and Nambaale Support Vector Machines Simon Alex and Nambaale MCS 7101 October 8, 2019 1 / 28

2. Overview 1 Support Vector Machines Applications of SVM What is Machine Learning? What Is SVM? Features of SVM How Does SVM Work? Non-Linear SVM SVM Use Case Simon Alex and Nambaale MCS 7101 October 8, 2019 2 / 28

3. Applications of Support Vector Machines Text Categorization Bioinformatics Face Recognition Image Classiﬁcation Simon Alex and Nambaale MCS 7101 October 8, 2019 3 / 28

4. What is Machine Learning? Simon Alex and Nambaale MCS 7101 October 8, 2019 4 / 28

5. Supervised Learning Simon Alex and Nambaale MCS 7101 October 8, 2019 5 / 28

6. Unsupervised Learning Simon Alex and Nambaale MCS 7101 October 8, 2019 6 / 28

7. Type of Problems in Machine Learning Simon Alex and Nambaale MCS 7101 October 8, 2019 7 / 28

8. Where are Support Vector Machines? Simon Alex and Nambaale MCS 7101 October 8, 2019 8 / 28

9. What is Support Vector Machine? Support Vector Machine (SVM) is a supervised learning method used for classiﬁcation and regression. SVM separates data using a hyperplane which acts like a decision boundary between the various classes. SVM works well for classifying higher-dimensional data (lots of features). Simon Alex and Nambaale MCS 7101 October 8, 2019 9 / 28

10. Features of Support Vector Machine SVM is a supervised learning algorithm. It can be used for both classiﬁcation and regression problems. SVM can be used for classifying non-linear data using the kernel trick. Simon Alex and Nambaale MCS 7101 October 8, 2019 10 / 28

11. How Does SVM Work? Simon Alex and Nambaale MCS 7101 October 8, 2019 11 / 28

12. How Does SVM Work? Simon Alex and Nambaale MCS 7101 October 8, 2019 12 / 28

13. What is a Support Vector in SVM Simon Alex and Nambaale MCS 7101 October 8, 2019 13 / 28

14. What is a Support Vector in SVM Simon Alex and Nambaale MCS 7101 October 8, 2019 14 / 28

15. What is a Support Vector in SVM Simon Alex and Nambaale MCS 7101 October 8, 2019 15 / 28

16. Non-Linear Support Vector Machine Simon Alex and Nambaale MCS 7101 October 8, 2019 16 / 28

17. Non-Linear Support Vector Machine Simon Alex and Nambaale MCS 7101 October 8, 2019 17 / 28

18. Deﬁning the Separating Hyperplane Form of equation deﬁning the decision surface separating the classes is a hyperplane of the form: wT x + b = 0 Where: w is a weight vector x is an input vector b is bias Simon Alex and Nambaale MCS 7101 October 8, 2019 18 / 28

19. Deﬁning the Separating Hyperplane Simon Alex and Nambaale MCS 7101 October 8, 2019 19 / 28

20. Support Vector Classiﬁcation In practice, SVM uses SVC to classify data. Diﬀerent kernels can be used with SVC. These kernels include: linear, RBF and polynomial. Some kernels work better than others for a given dataset. Simon Alex and Nambaale MCS 7101 October 8, 2019 20 / 28

21. Gamma and C Gamma controls the shape of the ’peaks’ where the points are raised. The gamma parameter deﬁnes the degree of non-linearity, with low values tending high linearity and high values leading to high non-linearity. Simon Alex and Nambaale MCS 7101 October 8, 2019 21 / 28

22. Inﬂuence of Gamma on Training Data Simon Alex and Nambaale MCS 7101 October 8, 2019 22 / 28

23. Gamma and C C controls the cost of misclassification on the training data. A high C tries to minimize the misclassification of training data leading to overfitting∗ and a low value tries to maintain a smooth classification. ∗ Underfitting is where the model neither performs well in training nor testing. Simon Alex and Nambaale MCS 7101 October 8, 2019 23 / 28

24. Inﬂuence of C on Training Data Simon Alex and Nambaale MCS 7101 October 8, 2019 24 / 28

25. Cross-Validation Cross-Validation is a statistical method of evaluating and comparing learning algorithms by dividing data into two segments: one used to learn or train a model and the other used to validate the model. The basic form of cross-validation is k-fold cross-validation. Simon Alex and Nambaale MCS 7101 October 8, 2019 25 / 28

26. K-Fold Cross Validation Simon Alex and Nambaale MCS 7101 October 8, 2019 26 / 28

27. Questions? Simon Alex and Nambaale MCS 7101 October 8, 2019 27 / 28

28. SVM Use Case Simon Alex and Nambaale MCS 7101 October 8, 2019 28 / 28

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