FIND-S Algorithm - simple question

The FIND-S algorithm is probably one of the most simple machine learning algorithms. However, I can't find many examples out there.. Just the standard 'sunny, rainy, play-ball' examples that's always used in machine learning. Please could someone help me with this applica开发者_开发百科tion (its a past exam question in machine learning).

Hypotheses are of the form a <= x <= b, c <= y <= d where x and y are points in an x,y plane and c and d are any integer. Basically, these hypotheses define rectangles in the x,y space.

These are the training examples where - is a negative example and + is a positive example and the pairs are the x,y co-ordinates:

 + 4, 4
 + 5, 3 
 + 6, 5 
 - 1, 3 
 - 2, 6 
 - 5, 1 
 - 5, 8 
 - 9, 4

All I want to do is apply FIND-S to this example! It must be simple! Either some tips or a solution would be awesome.

Thank you.

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Find-S seeks the most restrictive (ie most 'specific') hypothesis that fits all the positive examples (negatives are ignored).

In your case, there's an obvious graphical interpretation: "find the smallest rectangle that contains all the '+' coordinates"...

... which would be a=4, b=6, c=3, d=5.

The algorithm for doing it would be something like this:

Define a hypothesis rectangle h[a,b,c,d], and initialise it to [-,-,-,-]
for each + example e {
    if e is not within h {
        enlarge h to be just big enough to hold e (and all previous e's)
    } else { do nothing: h already contains e }
}

If we step through this with your training set, we get:

 0. h = [-,-,-,-] // initial value
 1. h = [4,4,4,4] // (4,4) is not in h: change h so it just contains (4,4)
 2. h = [4,5,3,4] // (5,3) is not in h, so enlarge h to fit (4,4) and (5,3)
 3. h = [4,6,3,5] // (6,5) is not in h, so enlarge again
 4. // no more positive examples left, so we're done.