1. Concept Learning Consider the hypothesis space defined over these instances, in which each hypothesis is...

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1. Concept Learning Consider the hypothesis space defined over these instances, in which each hypothesis is represented by a pair of 6-tuples, and where each attribute constraint may be a specific value, "?", or "O". Please provide a hand trace of the Candidate-Elimination algorithm learning from the example in Table land hypothesis language. Show the specific and general boundaries of the version space after it has processed the first training example, then second example, etc. Example Sky Temperature Wind Sunny Sunny Mild Sunny Rainy 1 2 3 4 5 Humidity Day Forecast Play High Weekend Same Yes Weekday Change High Yes High Weekday Change Low Weekday Same Sunny Mild Strong High Weekend Same Table 1: Positive and Negative training examples for the target concept Play Tennis Hot Mild Hot Strong Weak Strong Weak Yes No Yest Consider the hypothesis space defined over these instances, in which each hypothesis is represented by a pair of 4-tuples, and where each attribute constraint may be a specific value, "?", or "O". Please provide a hand trace of the Find-S algorithm learning from the example in Table 2 and hypothesis language. Example Taking Notes Attending Class Doing HW 1 2345 Yes Yes NO NO Yes Asking Questions Yes No NO YES YES Yes YES NO NO YES Yes YES NO YES YES Good Grades Yes YES NO NO YES 1. Concept Learning Consider the hypothesis space defined over these instances, in which each hypothesis is represented by a pair of 6-tuples, and where each attribute constraint may be a specific value, "?", or "O". Please provide a hand trace of the Candidate-Elimination algorithm learning from the example in Table land hypothesis language. Show the specific and general boundaries of the version space after it has processed the first training example, then second example, etc. Example Sky Temperature Wind Sunny Sunny Mild Sunny Rainy 1 2 3 4 5 Humidity Day Forecast Play High Weekend Same Yes Weekday Change High Yes High Weekday Change Low Weekday Same Sunny Mild Strong High Weekend Same Table 1: Positive and Negative training examples for the target concept Play Tennis Hot Mild Hot Strong Weak Strong Weak Yes No Yest Consider the hypothesis space defined over these instances, in which each hypothesis is represented by a pair of 4-tuples, and where each attribute constraint may be a specific value, "?", or "O". Please provide a hand trace of the Find-S algorithm learning from the example in Table 2 and hypothesis language. Example Taking Notes Attending Class Doing HW 1 2345 Yes Yes NO NO Yes Asking Questions Yes No NO YES YES Yes YES NO NO YES Yes YES NO YES YES Good Grades Yes YES NO NO YES

Answer rating: 100% (QA)

The images youve provided show two different machine learning problems that use concept learning algorithms The first shows the CandidateElimination algorithm and the second shows the FindS algorithm ... View the full answer