Thread: Q4) h(x) = ax
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Old 02-01-2013, 12:19 PM
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yaser yaser is offline
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Default Re: Q4) h(x) = ax

Originally Posted by Anne Paulson View Post
So, in this procedure we:

Pick two points;
Find the best slope a for those two points, the one that minimizes the squared error for those two points;
Do this N times and average all the as

Rather than:

Pick two points;
Calculate the squared error for those two points as a function of a;
Do this N times, then find the a that minimizes the sum of all of the squared errors, as we do with linear regression

Are we doing the first thing here or the second thing? Either way there's a simple analytic solution, but I'm not sure which procedure we're doing.
The first method estimates a for the average hypothesis {\bar g} (which takes into consideration only two points at a time). The second method estimates a for the best approximation of the target function (which takes into consideration all the points in the input space {\cal X} at once).
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