SOFT COMPUTING

1. What is Learning Vector Quantization (LVQ)?

a) A supervised learning algorithm for classification. b) An unsupervised clustering algorithm. c) A regression algorithm. d) A reinforcement learning algorithm.

2. What is the primary goal of LVQ?

a) To classify data by adjusting prototype vectors. b) To minimize the error in regression tasks. c) To cluster data without labels. d) To maximize the reward in reinforcement learning.

3. What is a Radial Basis Function Neural Network (RBFNN)?

a) A neural network that uses radial basis functions as activation functions. b) A type of database management system. c) A programming language for AI. d) A hardware component used in robotics.

4. What is the role of the "hidden layer" in an RBFNN?

a) To transform input data using radial basis functions. b) To store the input data. c) To display the final output. d) To connect the network to external devices.

5. What is a Support Vector Machine (SVM)?

a) A supervised learning algorithm for classification and regression. b) An unsupervised clustering algorithm. c) A reinforcement learning algorithm. d) A type of database management system.

6. What is the primary goal of an SVM?

a) To find the optimal hyperplane that separates data into classes. b) To minimize the error in regression tasks. c) To cluster data without labels. d) To maximize the reward in reinforcement learning.

7. Which of the following is a kernel function used in SVM?

a) Linear kernel. b) Polynomial kernel. c) Radial Basis Function (RBF) kernel. d) All of the above.

8. What is the role of the "margin" in SVM?

a) To maximize the distance between the hyperplane and the nearest data points. b) To store the input data. c) To visualize the results. d) To connect the network to external devices.

9. What is the main advantage of RBFNN over traditional Neural Networks?

a) Faster training for certain types of data. b) Better performance for high-dimensional data. c) Ability to handle non-linear data. d) All of the above.

10. What is the primary difference between LVQ and SVM?

a) LVQ is a prototype-based method, while SVM is a margin-based method. b) LVQ is unsupervised, while SVM is supervised. c) LVQ is used for regression, while SVM is used for classification. d) LVQ is faster than SVM.

11. Which of the following is true about RBFNN?

a) It uses radial basis functions in the hidden layer. b) It is a type of supervised learning algorithm. c) It is used for both classification and regression. d) All of the above.

12. What is the role of the "kernel trick" in SVM?

a) To transform data into a higher-dimensional space for separation. b) To store the input data. c) To visualize the results. d) To connect the network to external devices.

LVQ, RBFNN, and SVM MCQs

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