Support Vector Machine and Implementation using Weka
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The document provides an introduction to machine learning, explaining its definition and significance in modern computing, alongside various methods like supervised and unsupervised learning. It details Support Vector Machine (SVM) as a classification algorithm that constructs hyperplanes to separate data points and introduces key concepts such as margin and support vectors. Additionally, it mentions Weka as a tool used for implementing machine learning practices, including SVM with the Iris dataset.
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:
- 21. Types of Data: Linearly Separable
- 22. Non - Linearly Separable Data
- 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
- 31. IRIS Dataset Attributes: 1. Sepal Length 2. Sepal Width 3. Petal Length 4. Petal Width
- 32. IRIS Flowers
- 34. Implementation of SVM in WEKA
- 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