Support vector-machines-presentation

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

Applications of Support Vector Machines
Text Categorization Bioinformatics Face Recognition Image Classiﬁcation

What is Machine Learning?

Supervised Learning

Unsupervised Learning

Type of Problems in Machine Learning

Where are Support Vector Machines?

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).

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.

How Does SVM Work?

What is a Support Vector in SVM

Non-Linear Support Vector Machine

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

Deﬁning the Separating Hyperplane

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.

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.

Inﬂuence of Gamma on Training Data

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.

Inﬂuence of C on Training Data

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.

K-Fold Cross Validation

Questions?

SVM Use Case

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