CS607-Artificial Intelligence Quiz MCQS #Objective #Questions #FinalTerm
1. Inductive Learning is based on the knowledge that if something happens a lot it is likely to be generally ___
- True ✔
2. If the antecedent is only partially true, then the output fuzzy set is truncated according to the ___ method.
- Implication ✔
3. The input of aggregation process is the list of truncated output functions returned by the ___ process for each rule.
- Implication ✔
4. ___ learning works on existing facts and knowledge and deduces new knowledge from the old.
- Deductive ✔
5. Machine learning typically follows ___ phases according to Finlay.
6. Which one is NOT the phase of machine learning:
- None ✔
7. In theoretical computer science there are two main branches of problems.
- Tractable and Intractable ✔
- Intractable and Induction
- Tractable and Induction
8. ___ is the process by which the fuzzy sets that represent the outputs of each rule are combined into a single fuzzy set.
- Aggregation ✔
9. Outputs of learning are determined by the ___
- Application ✔
10. ___ is the process of formulating the mapping from a given input to an output using Fuzzy logic.
11. The brain is a collection of about 100 ___ interconnected neurons.
- Billion ✔
12. A single Perceptron simply draws a line, which is a hyper plane when the data is ___ than/to two (2) dimensional.
13. In Candidate-Elimination algorithm version space is represented by two sets named:
- G and S ✔
- G and F
- S and F
- H and S
14. The first step of FIND-S is to initialize h to the most specific hypothesis in ___ : h<>
15. Decision trees give us disjunctions of conjunctions, that is, they have the form: (A AND B) ___ (C AND D)
16. Interactive Dichotomizer uses a special function ___, to evaluate the gain information of each attribute.
17. Measure of the effectiveness of an attribute in classifying the training data is called
- Information Gain ✔
- Measure Gain
- Information Goal
18. Artificial Neural Networks is a new learning paradigm which takes its roots from ___ inspired approach to learning.
- Biology ✔
19. Which one is NOT the advantage of Neural Network
- Excellent for pattern recognition
- Excellent classifiers
- Handles noisy data well
- None ✔
20. In all calculations involving Entropy we define ___ to be ___
- 0 log 0, 0 ✔
- 0 log 10, 1
- 0 log 0, 1
- 1 log 1, 1