Support vector machines (svm)

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A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.

Ingénierie

Support Vector Machines
(SVM)
SS_PATIL12-02-2020 1

Support Vector Machines (SVM)
- Supervised learning methods
- Associated learning algorithms
- They can represent linear & non-linear functions and they have
an efficient training algorithm
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There are 2 kinds of SVM classifiers:
1.Linear SVM Classifier
2.Non-Linear SVM Classifier
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• Example:
Fig. 1 Fig. 2
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 Example of Bad Decision Boundaries
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 Tuning Parameters
 Kernel
 Regularization
 Gamma
 Margin.
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 Kernel
linear kernel
f(x) = B(0) + sum(ai * (x,xi))
Polynomial kernel
K(x,xi) = 1 + sum(x * xi)^d
exponential Kernel
K(x,xi) = exp(-gamma * sum((x — xi²)).
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 Regularization
To avoid misclassifying each training example
low regularization value high regularization value
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 Margin
separation of line to the closest class points.
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Problem Statement: Use Machine Learning to predict cases of breast cancer
using patient treatment history and health data.
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Dataset: Breast Cancer Wisconsin (Diagnostic)
Dataset
Let us have a quick look at the dataset
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Step 1: Load Pandas library and the dataset using Pandas
Step 2: Define the features and the target
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Have a look at the features:
SS_PATIL12-02-2020 16

Step 3: Split the dataset into train and test using sklearn before building the SVM algorithm
model
Step 4: Import the support vector classifier function or SVC function from Sklearn SVM module. Build the Support
Vector Machine model with the help of the SVC function
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Step 5: Predict values using the SVM algorithm model
Step 6: Evaluate the Support Vector Machine model
SS_PATIL12-02-2020 19

 SVM Applications:
• SVMS are a by product of Neural Network. They are widely applied to pattern
classification and regression problems. Here are some of its applications:
• Facial expression classification
• Speech recognition
• Handwritten digit recognition
SS_PATIL12-02-2020 20

 Advantages of SVM Classifier:
 More effective
 It works effectively
 Memory efficient
 It is a robust model
SS_PATIL12-02-2020 21

 Disadvantages of SVM Classifier:
• Not suitable
• It does not perform very well
• good generalization performance
• SVMs have high algorithmic complexity and extensive memory requirements due to the use of quadratic
programming.
SS_PATIL12-02-2020 22

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Support vector machines (svm)

1. Support Vector Machines (SVM) SS_PATIL12-02-2020 1

2. Support Vector Machines (SVM) - Supervised learning methods - Associated learning algorithms - They can represent linear & non-linear functions and they have an efficient training algorithm SS_PATIL12-02-2020 2

3. SS_PATIL12-02-2020 3

4. There are 2 kinds of SVM classifiers: 1.Linear SVM Classifier 2.Non-Linear SVM Classifier SS_PATIL12-02-2020 4

5. • Example: Fig. 1 Fig. 2 SS_PATIL12-02-2020 5

6. • Hyperplane SS_PATIL12-02-2020 6

7.  Example of Bad Decision Boundaries SS_PATIL12-02-2020 7

8.  Tuning Parameters  Kernel  Regularization  Gamma  Margin. SS_PATIL12-02-2020 8

9.  Kernel linear kernel f(x) = B(0) + sum(ai * (x,xi)) Polynomial kernel K(x,xi) = 1 + sum(x * xi)^d exponential Kernel K(x,xi) = exp(-gamma * sum((x — xi²)). SS_PATIL12-02-2020 9

10.  Regularization To avoid misclassifying each training example low regularization value high regularization value SS_PATIL12-02-2020 10

11.  Gamma SS_PATIL12-02-2020 11

12.  Margin separation of line to the closest class points. SS_PATIL12-02-2020 12

13. Problem Statement: Use Machine Learning to predict cases of breast cancer using patient treatment history and health data. SS_PATIL12-02-2020 13

14. Dataset: Breast Cancer Wisconsin (Diagnostic) Dataset Let us have a quick look at the dataset SS_PATIL12-02-2020 14

15. Step 1: Load Pandas library and the dataset using Pandas Step 2: Define the features and the target SS_PATIL12-02-2020 15

16. Have a look at the features: SS_PATIL12-02-2020 16

17. SS_PATIL12-02-2020 17

18. Step 3: Split the dataset into train and test using sklearn before building the SVM algorithm model Step 4: Import the support vector classifier function or SVC function from Sklearn SVM module. Build the Support Vector Machine model with the help of the SVC function SS_PATIL12-02-2020 18

19. Step 5: Predict values using the SVM algorithm model Step 6: Evaluate the Support Vector Machine model SS_PATIL12-02-2020 19

20.  SVM Applications: • SVMS are a by product of Neural Network. They are widely applied to pattern classification and regression problems. Here are some of its applications: • Facial expression classification • Speech recognition • Handwritten digit recognition SS_PATIL12-02-2020 20

21.  Advantages of SVM Classifier:  More effective  It works effectively  Memory efficient  It is a robust model SS_PATIL12-02-2020 21

22.  Disadvantages of SVM Classifier: • Not suitable • It does not perform very well • good generalization performance • SVMs have high algorithmic complexity and extensive memory requirements due to the use of quadratic programming. SS_PATIL12-02-2020 22

23. Thank You…. SS_PATIL12-02-2020 23

Support vector machines (svm)

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