Lazy - Locally Weighted Learning Machine Learning Dr....

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Locally Weighted Learning Machine Learning Dr. Barbara Hammer
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Locally Weighted Learning Instance-based Learning (“Lazy Learning”) Local Models k-Nearest Neighbor Weighted Average Locally weighted regression Case-based reasoning
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When to consider Nearest Neighbor  Algorithm? Instances map to points in R n Less then 20 attributes per instance Lots of training data Advantages: Training is very fast Learning complex target functions Don’t lose information Disadvantages: Slow at query Easily fooled by irrelevant attributes
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k-Nearest Neighbor Algorithm  (Classification) Let an arbitrary instances be described:    x={a 1 (x), a 2 (x), . .., a n (x)} The distance between two instances and is defined: = - n r j r i r x a x a d 1 2 )) ( ) ( (
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k-Nearest Neighbor Algorithm Training Algorithm: Store all training examples < x, f(x) > Classification Algorithm: Given a query instance
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Lazy - Locally Weighted Learning Machine Learning Dr....

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