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인공지능 및 기계학습 개론 I - Ch2.2
Smiling Sammy
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2022년 4월 28일
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인공지능 및 기계학습 개론 I
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인공지능 및 기계학습 개론 I
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Introduction to Rule Based Algorithm
Find S Algorithm
Initialize h to the most specific in H
empty set -> include x1 -> include x2 ... -> include xn
Version Space
Many hypotheses possible, and No way to find the convergence
Need to setup the perimeter of the possible hypothesis
The set of the possible hypothesis == Version Space, VS
General Boundary, G
Specific Boundary, S
Candidate Elimination Algorithm
initialize S to maximally specific h in H
initialize G to maximally general h in H
positive: change S
negative: change G
Progress
How to classify the next instance?
Some example
General boundary -> O
Speicific boundary -> X
???? => disadvantage of rule based learning
Is this working?
working on the perfect world
but we don't live in the perfect world
any noise in o of D
decision factor other than o of x
a correct h can be removed by the noise -> cannot say yes and no
Smiling Sammy
Data Scientist, Data Analyst
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인공지능 및 기계학습 개론 I - Ch2.1
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인공지능 및 기계학습 개론 I - Ch2.3
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