- 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) Adding noise to the data
B) Removing outliers
C) Simplifying the decision boundary
D) Mapping data into higher-dimensional space
- 3. Which kernel is commonly used in SVM for non-linear classification?
A) RBF (Radial Basis Function)
B) Sigmoid kernel
C) Linear kernel
D) Polynomial kernel
- 4. What is regularization parameter C in SVM?
A) Number of support vectors
B) Trade-off between margin and error
C) Kernel parameter
D) Number of dimensions
- 5. What is the loss function used in SVM?
A) Mean squared error
B) Cross-entropy loss
C) Hinge loss
D) L2 regularization
- 6. Which optimization algorithm is commonly used in SVM training?
A) Adam
B) Sequential Minimal Optimization (SMO)
C) Newton's Method
D) Gradient Descent
- 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) Calculating margin width
B) Mapping input data into a higher-dimensional space
C) Updating model weights
D) Selecting support vectors
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