• Draw the context level diagram.
• Identify Entities, their attributes, relationships among entities in above scenario and draw Entity Relationship Diagram (ERD) for it.
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.
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!