Machine Learning | Candidate Elimination Algorithm
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- Опубликовано: 10 мар 2020
- Candidate-Elimination algorithm performs a bidirectional search in the hypothesis space. It maintains a set, S, of most specific hypotheses that are consistent with the training data and a set, G, of most general hypotheses consistent with the training data. #ConceptLearning #CandidateEliminationAlgorithm
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where is this example solved handwritten in the above git link....i mean in which assignment of your notes?
@@swapnilghoge7433 , Inside the folder 📁 kindly find under Assignment 3.3 , not the same example but a similar question has been solved
great tutorial, keep up the good work . Thank you for this video.
you just calculated the most specific and most general boundary. you have to show the version space that is the intermediate boundary.
thanks
Before solving problem we should explain our main goal that what are we going to achieve by solving it. which is missing and created a lot of confusion for me.
Did not understand how you discard that
I have confusion at the elimination point plz clear it
Tqq sir
Sir also make videos on how to match the instances with hypothesis space diagram in machine learning asap
at 11:29 what is your motive what you saying sir please explain
did not understand the discard part
At the end of I didn't know result that what was our primary goal for solving it.
In the middle of the video your explanation became very clumsy