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Machine learning algorithms are helpful in solving the complex problems. Furthermore, in Machine learning it is impossible to exhaustively search over the entire concept space i.e. used for representing the problem with respect to some given attributes. Secondly during training process learner has to hypothesize to match the output that best fits the true output of the concept space.
Discuss and compare that which algorithm is best suitable for either general to specific or specific to general ordering of hypothesis space.
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give some idea solution
Yeah please give :(
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solution Idea : In general, machine learning algorithms apply In general, machine learning algorithms apply some optimization algorithm to find a good some optimization algorithm to find a good hypothesis. In this case, hypothesis. In this case,
J is piecewise piecewise
constant constant, which makes this a difficult problem , which makes this a difficult problem
The maximum likelihood estimate Direct Computation. The maximum likelihood estimate
of P(x,y) can be computed from the data without search. ) can be computed from the data without search.
However, inverting the However, inverting the Σ matrix requires O(n matrix requires O(n3) time.
General-to-Specific Ordering of Hyothesis
g does not depend on the concept to be learned
• It defines a partial order over the set of hypotheses
• strictly-more-general than: >
• Basis for the learning algorithms presented in the following!
– Start with most specific hypothesis
∅, ∅, ∅, ∅, ∅, ∅
– Generalize if positive example is not covered!
In general, machine learning algorithms apply some optimization algorithm to find a good some optimization algorithm to find a good hypothesis. In this case, hypothesis. In this case,
J is piecewise constant, which makes this a difficult problem. The maximum likelihood estimates Direct Computation. The maximum likelihood estimate of P(x, y) can be computed from the data without search. ) can be computed from the data without search. However, inverting the Σ matrix requires O(n matrix requires O(n3) time.
which one is 100% correct?
Is this correct ???/
Candidate-Elimination algorithm converge toward the true target concept, provided
There are no errors in the training examples If there is error: The algorithm will remove the true target concept from the version space, because it will eliminate all hypotheses that are inconsistent with each training example.
So, this will be the best suited algorithm for specific to general problems.
Inductive Learning A hypothesis (e.g. a classifier) that is consistent with a sufficiently large number of representative training examples is likely to accurately classify novel instances drawn from the same universe and hence it will be better if general to specific hypothesis are given to this algorithm
So, this will be the best suited algorithm for general to specific problems.