Support Vector Machines ( SVM )
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Welcome to the Supervised Machine Learning and Data Sciences. Algorithms for building models. Support Vector Machines. Classification algorithm explanation and code in Python ( SVM ) .
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Support Vector Machine ppt presentationAyanaRukasar 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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Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
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Exploring Wayland: A Modern Display Server for the FutureICS Wayland is revolutionizing the way we interact with graphical interfaces, offering a modern alternative to the X Window System. In this webinar, we’ll delve into the architecture and benefits of Wayland, including its streamlined design, enhanced performance, and improved security features.
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TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.
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Exploring Code Comprehension in Scientific Programming: Preliminary Insight...University of Hawai‘i at Mānoa This presentation explores code comprehension challenges in scientific programming based on a survey of 57 research scientists. It reveals that 57.9% of scientists have no formal training in writing readable code. Key findings highlight a "documentation paradox" where documentation is both the most common readability practice and the biggest challenge scientists face. The study identifies critical issues with naming conventions and code organization, noting that 100% of scientists agree readable code is essential for reproducible research. The research concludes with four key recommendations: expanding programming education for scientists, conducting targeted research on scientific code quality, developing specialized tools, and establishing clearer documentation guidelines for scientific software.
Presented at: The 33rd International Conference on Program Comprehension (ICPC '25)
Date of Conference: April 2025
Conference Location: Ottawa, Ontario, Canada
Preprint: https://arxiv.org/abs/2501.10037
Exploring Code Comprehension in Scientific Programming: Preliminary Insight...
Exploring Code Comprehension in Scientific Programming: Preliminary Insight...University of Hawai‘i at Mānoa
Support Vector Machines ( SVM )
- 1. Support Vector Machine Classification , Regression and Outliers detection Khan
- 2. Introduction SVM A Support Vector Machine (SVM) is a discriminative classifier which intakes training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.
- 3. What could be drawn to classify the black dots from blue squares?
- 4. A line drawn between these data points classify the black dots and blue squares. Linearly separable data
- 5. Linear vs Nonlinear separable data
- 6. What could be drawn to classify these data points ( red dots from blue stars )? NonLinearly separable data
- 7. Here the hyperplane is a 2d plane drawn parallel to x-axis that is the separator. NonLinearly separable data
- 8. Non Linear data ( type 2 ) Raw Data Line as Hyperplane
- 9. For the previous data the line , if used as a Hyperplane ● Two black dots also fall in category of blue squares ● Data separation is not perfect ● It tolerates some outliers in the classification
- 10. This type of separator best provides the classification. But ● It is quite difficult to train a model like this . ● This is termed as Regularisation parameter.
- 11. Tuning Parameters SVM 1. Kernel 2. Regularization 3. Gamma 4. Margin
- 12. Margin Margin is the perpendicular distance between the closest data points and the Hyperplane ( on both sides ) The best optimised line ( hyperplane ) with maximum margin is termed as Margin Maximal Hyperplane. The closest points where the margin distance is calculated are considered as the support vectors.
- 14. Regularization ● Also the ‘ C ‘ parameter in Python’s SkLearn Library ● Optimises SVM classifier to avoid misclassifying the data. ● C → large Margin of hyperplane → small ● C → small Margin of hyperplane → large ● misclassification(possible) 1. C ---> large , chance of overfit 2. C ---> small , chance of underfitting
- 16. Gamma ● Defines how far influences the calculation of of plausible line of separation. ● Low gamma -----> points far from plausible line are considered for calculation ● High gamma -----> points close to plausible line are considered for calculation
- 17. High Gamma Value Low Gamma Value
- 18. Kernels ● Mathematical functions for transforming data ● using some linear algebra ● Different SVM algorithms use different types of kernel functions
- 19. Various kernels available 1. Linear kernel 2. Non - linear kernel 3. Radial basis function ( RBF ) 4. Sigmoid 5. Polynomial 6. Exponential
- 20. Example : K(x, y) = <f(x), f(y)> Kernel function dot product of n- dimensional inputs
- 21. Mathematical representation x = (x1, x2, x3); y = (y1, y2, y3) f(x) = (x1x1, x1x2, x1x3, x2x1, x2x2, x2x3, x3x1, x3x2, x3x3) f(y) = (y1y1, y1y2, y1y3, y2y1, y2y2, y2y3, y3y1, y3y2, y3y3) K(x, y ) = (<x, y>)² x = (1, 2, 3) y = (4, 5, 6) f(x) = (1, 2, 3, 2, 4, 6, 3, 6, 9) f(y) = (16, 20, 24, 20, 25, 30, 24, 30, 36) <f(x), f(y)> = 16 + 40 + 72 + 40 + 100+ 180 + 72 + 180 + 324 = 1024 K(x, y ) = (4 + 10 + 18)² = 1024 ----> Kernel function
- 22. Pros : ● It works really well with clear margin of separation ● It is effective in high dimensional spaces. ● It is effective in cases where number of dimensions is greater than the number of samples. ● It uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
- 23. Cons : ● It doesn’t perform well, when we have large data set because the required training time is higher ● It also doesn’t perform very well, when the data set has more noise i.e. target classes are overlapping ● SVM doesn’t directly provide probability estimates, these are calculated using an expensive five-fold cross-validation. It is related SVC method of Python scikit-learn library.
- 24. Applications : 1. Face detection 2. Text and hypertext categorization 3. Classification of images 4. Bioinformatics 5. Handwriting recognition 6. Protein fold and remote homology detection 7. Generalized predictive control(GPC)
- 25. Let’s code now Data used : Iris from Sklearn Plots : Matplotlib Kernels : Linear and rbf File : svm_final.py Link to code : Click here for code
- 26. Thank You