gs gs126 Version Spaces - Candidate Elimination Algorithm - Quiz No.1
gs gs126 Neural Networks Quiz
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Question 1: The algorithm is trying to find a suitable day for swimming. What is the most general hypothesis?
A rainy day is a positive example
A sunny day is a positive example
No day is a positive example
Every day is a positive example
Question 2: Candidate-Elimination algorithm can be described by ____________
just a set of candidate hypotheses
depends on the dataset
set of instances, set of candidate hypotheses
just a set of instances
Question 3: How is the version space represented?
Least general members
Most general members
Most general and least general members
Arbitrary members chosen form hypothesis space
Question 4: Let G be the set of maximally general hypotheses. While iterating through the dataset, when is it changed for the first time?
Negative example is encountered for the first time
Positive example is encountered for the first time
First example encountered, irrespective of whether it is positive or negative
S, the set of maximally specific hypotheses, is changed
Question 5: Let S be the set of maximally specific hypotheses. While iterating through the dataset, when is it changed for the first time?
Negative example is encountered for the first time
Positive example is encountered for the first time
First example encountered, irrespective of whether it is positive or negative
G, the set of maximally general hypotheses, is changed
Question 6: S = <sunny, warm, high, same>. Training data = <sunny, warm, normal, same> => Yes (positive example). How will S be represented after encountering this training data?
<sunny, warm, high, same>
<phi, phi, phi, phi>
<sunny, warm, ?, same>
<sunny, warm, normal, same>
Question 7: S = <phi, phi, phi, phi>Training data = <rainy, cold, normal, change> => No (negative example). How will S be represented after encountering this training data?
<phi, phi, phi, phi>
<sunny, warm, high, same>
<rainy, cold, normal, change>
<?, ?, ?, ?>
Question 8: G = <?, ?, ?, ?>. Training data = <sunny, warm, normal, same> => Yes (positive example). How will G be represented after encountering this training data?
<sunny, warm, normal, same>
<phi, phi, phi, phi>
<rainy, cold, normal, change>
<?, ?, ?, ?>
Question 9: G = (<sunny, ?, ?, ?> ; <?, warm, ?, ?> ; <?, ?, high, ?>). Training data = <sunny, warm, normal, same> => Yes (positive example). How will G be represented after encountering this training data?
<phi, phi, phi, phi>
(<sunny, ?, ?, ?> ; <?, warm, ?, ?> ; <?, ?, high, ?>)
(<sunny, ?, ?, ?> ; <?, warm, ?, ?>)
<?, ?, ?, ?>
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