- Multiple Choice 1 (174.74 KiB) Viewed 36 times
multiple choice
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answerhappygod
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multiple choice
multiple choice
Here are the questions: Q1. The problem of finding a decision boundary in SVM can be formulated as an optimisation problem using Lagrange multipliers. What is the goal? a) To maximize the margin of the decision boundary. b) To minimize the margin of the decision boundary. Q2. After SVM learning, each Lagrange multiplier lambda_i takes either zero or non-zero value. Which one is correct: a) A non-zero lambda_i indicates that example i is a support vector. b) A zero lambda_i indicates that example i is a support vector. c) A non-zero lambda_i indicates that the learning has not yet converged to a global minimum. d) A zero lambda_i indicates that the learning process has identified support for example i. Q3. In linear SVM, during training we compute dot products between: a) training vectors b) training and testing vectors c) support vectors d) support vectors and Lagrange multiplayers Q4. Bagging is only applicable to classification problems and cannot be applied to regression problems. a) True b) False Q5. Boosting is guaranteed to improve the performance of the single classifier it uses. a) True b) False
Here are the questions: Q1. The problem of finding a decision boundary in SVM can be formulated as an optimisation problem using Lagrange multipliers. What is the goal? a) To maximize the margin of the decision boundary. b) To minimize the margin of the decision boundary. Q2. After SVM learning, each Lagrange multiplier lambda_i takes either zero or non-zero value. Which one is correct: a) A non-zero lambda_i indicates that example i is a support vector. b) A zero lambda_i indicates that example i is a support vector. c) A non-zero lambda_i indicates that the learning has not yet converged to a global minimum. d) A zero lambda_i indicates that the learning process has identified support for example i. Q3. In linear SVM, during training we compute dot products between: a) training vectors b) training and testing vectors c) support vectors d) support vectors and Lagrange multiplayers Q4. Bagging is only applicable to classification problems and cannot be applied to regression problems. a) True b) False Q5. Boosting is guaranteed to improve the performance of the single classifier it uses. a) True b) False
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