The Find-S algorithm finds the most specific hypothesis that is consistent with positive training examples by starting with the most specific hypothesis and gradually generalizing it only as far as needed to be consistent with each new positive example seen. The final hypothesis output by Find-S will be the most specific hypothesis within the hypothesis space that is consistent with all positive examples, and also consistent with negative examples if the target concept is representable. Consistency means the hypothesis agrees with all training examples - it outputs the correct label for each example.

1. Find – S, Consistent Hypothesis
and Version Space

2. Find-S Algorithm
• The Find-S algorithm finds the maximally specific hypothesis that satisfies a set of
training values.
• To do so it works its way from the most specific hypothesis and gradually becomes
more general.
1. Initialize h to the most specific hypothesis in H
2. For each positive training instance x
For each attribute constraint ai in h
IF the constraint ai in h is satisfied by x THEN
do nothing
ELSE
replace ai in h by next more general constraint satisfied by x
3. Output hypothesis h

3. Find-S Algorithm
This algorithm tested only positive example.

14. The search begins (ho) with the most specific hypothesis in H, then considers increasingly
general hypotheses (hl through h4) as mandated by the training examples.
The search moves from hypothesis to hypothesis, searching from the most specific to
progressively more general hypotheses along one chain of the partial ordering.
At each step, the hypothesis is generalized only as far as necessary to cover the new positive
example. Therefore, at each stage, the hypothesis is the most specific hypothesis consistent with the
training examples observed up to this point.

15. The key property of the FIND-S algorithm
• FIND-S is guaranteed to output the most specific hypothesis within H
that is consistent with the positive training examples.
• FIND-S algorithm’s final hypothesis will also be consistent with the
negative examples provided the correct target concept is contained in H,
and provided the training examples are correct.

16. Consistent and Version Space
Consistent
A hypothesis h is consistent with a set of training examples D if and only if h(x)
= c(x) for each example (x, c(x)) in D.
Difference between definitions of consistent and satisfies
• A training example x is said to satisfy hypothesis h when h(x) = 1, regardless of
whether x is a positive or negative example of the target concept.
• An example x is said to consistent with hypothesis h if h(x) = c(x).

18. • The same set of training examples D are below.
Also we found the hypothesis h = < Sunny, Warm, ?, Strong, ?, ?>. Now for each example (x, c(x)) in
D, we will evaluate h(x) equals c(x).
• (<Sunny, Warm, Normal, Strong, Warm, Same>,<yes>) → h(x)=c(x)
• (<Sunny, Warm, High, Strong, Warm, Same>,<yes>) → h(x)=c(x)
• (<Rainy, Cold, High, Strong, Warm, Change>,<No>) → h(x)=c(x)
• (<Sunny, Warm, High, Strong, Cool, Change>,<yes>) → h(x)=c(x)

25. Example