Support Vector Machine ppt presentation
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Support vector machines (SVM) is a supervised machine learning algorithm used for both classification and regression problems. However, it is primarily used for classification. The goal of SVM is to create the best decision boundary, known as a hyperplane, that separates clusters of data points. It chooses extreme data points as support vectors to define the hyperplane. SVM is effective for problems that are not linearly separable by transforming them into higher dimensional spaces. It works well when there is a clear margin of separation between classes and is effective for high dimensional data. An example use case in Python is presented.
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Support vector machine, machine learning22054561 Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression by finding an optimal hyperplane that maximizes the margin between different classes. It identifies support vectors, which are the closest data points to the hyperplane, ensuring robustness to overfitting. When data is linearly separable, SVM finds a straight-line boundary, while for non-linear cases, it uses kernel functions (like polynomial, RBF, or sigmoid) to map data into a higher-dimensional space where it becomes separable. The optimization problem aims to minimize the norm of the weight vector while satisfying margin constraints. With advantages like effective handling of high-dimensional data and suitability for both small and large datasets, SVM is widely applied in text classification, image recognition, and bioinformatics.SVM operates by mapping input features into a high-dimensional space and constructing a decision boundary (hyperplane) that best separates different classes. The optimal hyperplane is chosen to maximize the margin, which is the distance between the closest data points (support vectors) from each class to the hyperplane.SVM’s performance largely depends on the choice of kernel functions, which transform input data into a higher-dimensional space where it becomes easier to classify. Commonly used kernels include the linear kernel for simple separable data, the polynomial kernel for capturing interactions between features, and the radial basis function (RBF) kernel for complex, non-linear patterns. The regularization parameter
𝐶
C controls the trade-off between maximizing the margin and minimizing classification errors, with higher values leading to lower bias but higher variance. Additionally, the soft margin approach allows some misclassification by introducing slack variables, making SVM more flexible in handling noisy data.
Despite its strengths, SVM has limitations, such as high computational cost for large datasets and difficulty in selecting the right kernel and hyperparameters. Training time increases significantly as the number of samples grows, especially for non-linear SVMs using complex kernels. Moreover, SVM does not provide direct probability estimates, requiring additional techniques like Platt scaling for probabilistic outputs. However, with proper tuning and kernel selection, SVM remains a powerful tool in various domains, including natural language processing, medical diagnosis, and financial fraud detection.
Software defect estimation using machine learning algorithms
Software defect estimation using machine learning algorithmsVenkat Projects output screens,
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ramdom forest tree,
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Machine learning Algorithms,
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- 1. Machine Learning SUPPORT VECTOR MACHINE
- 2. Agenda • What is Machine Learning? • Why Support Vector Machine? • What is Support Vector Machine? • Understanding Support Vector Machine • Advantages of Support Vector Machine • Use Case in Python
- 3. What is Machine Learning ? • Machine Learning is a subset of Artificial Intelligence .It focuses mainly on the designing of systems, thereby allowing them to learn and make predictions based on some experience which is data in case of machines.
- 5. SUPERVISED LEARNING Machine Learning model learns from the past input data and makes future prediction as output.
- 7. Why SVM & its Case Study?
- 10. SVM • SVM is a supervised learning method that looks at data and sorts it into one of the two categories.
- 12. UNDERSTANDING SVM ? • Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning. • The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane. • SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine. Consider the below diagram in which there are two different categories that are classified using a decision boundary or hyperplane:
- 13. GOAL & it’s Keywords • The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane. • Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.
- 14. • Margin is distance from the decision surface to the closest data point Positive Hyperplane + Negative Hyperplane =Margin Suppose, D1+D2=M • Linearly Separable
- 15. KERNAL
- 16. NOT LINEARLY SEPARABLE • 1-D to 2-D
- 17. 2-D to 3-D
- 18. But How does the prediction works ?
- 19. USE CASE – PROBLEM STATEMENT
- 22. SVM Use Cases
- 23. ADVANTAGES • SVM works relatively well when there is a clear margin of separation between classes. • SVM is more effective in high dimensional spaces. • SVM is relatively memory efficient.
- 24. Use Case in PYTHON ?