- 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) Speech recognition
B) Video editing
C) Classification and regression
D) Image processing
- 2. What is the kernel trick in SVM?
A) Mapping data into higher-dimensional space
B) Simplifying the decision boundary
C) Removing outliers
D) Adding noise to the data
- 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) Kernel parameter
B) Number of support vectors
C) Number of dimensions
D) Trade-off between margin and error
- 5. What is the loss function used in SVM?
A) Cross-entropy loss
B) L2 regularization
C) Mean squared error
D) Hinge loss
- 6. Which optimization algorithm is commonly used in SVM training?
A) Adam
B) Newton's Method
C) Gradient Descent
D) Sequential Minimal Optimization (SMO)
- 7. What is the kernel trick in SVM used for?
A) Efficiently handling non-linear separable data
B) Preventing overfitting
C) Removing noise in the data
D) Simplifying the model complexity
- 8. What is the role of the kernel function in SVM?
A) Selecting support vectors
B) Updating model weights
C) Mapping input data into a higher-dimensional space
D) Calculating margin width
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