- 1. A support vector machine (SVM) is a supervised machine learning algorithm that is commonly used for classification and regression tasks. The goal of SVM is to find the hyperplane that best separates the data points into different classes, with a clear margin between the classes. SVM works by mapping the input data into a high-dimensional feature space and finding the optimal hyperplane that maximizes the margin between the classes. This optimal hyperplane is found by solving an optimization problem that aims to minimize the classification error and maximize the margin. SVM is known for its ability to handle high-dimensional data and complex classification tasks. It is also effective in dealing with non-linear data by using kernel functions to map the data into a higher-dimensional space. SVM is widely used in various applications such as text classification, image recognition, and bioinformatics due to its flexibility, accuracy, and robustness.
What is a Support Vector Machine (SVM) used for?
A) Video editing
B) Image processing
C) Speech recognition
D) Classification and regression
- 2. What is the kernel trick in SVM?
A) Mapping data into higher-dimensional space
B) Adding noise to the data
C) Simplifying the decision boundary
D) Removing outliers
- 3. Which kernel is commonly used in SVM for non-linear classification?
A) Sigmoid kernel
B) Polynomial kernel
C) Linear kernel
D) RBF (Radial Basis Function)
- 4. What is regularization parameter C in SVM?
A) Number of support vectors
B) Kernel parameter
C) Trade-off between margin and error
D) Number of dimensions
- 5. What is the loss function used in SVM?
A) L2 regularization
B) Hinge loss
C) Cross-entropy loss
D) Mean squared error
- 6. Which optimization algorithm is commonly used in SVM training?
A) Sequential Minimal Optimization (SMO)
B) Newton's Method
C) Gradient Descent
D) Adam
- 7. What is the kernel trick in SVM used for?
A) Efficiently handling non-linear separable data
B) Removing noise in the data
C) Simplifying the model complexity
D) Preventing overfitting
- 8. What is the role of the kernel function in SVM?
A) Mapping input data into a higher-dimensional space
B) Updating model weights
C) Selecting support vectors
D) Calculating margin width
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