lec05-InstanceBased - INSTANCE-BASE LEARNING Instance-based...

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INSTANCE-BASE LEARNING Instance-based learning methods simply store the training examples instead of learning explicit description of the target function. Generalizing the examples is postponed until a new instance must be classified. When a new instance is encountered, its relationship to the stored examples is examined in order to assign a target function value for the new instance. Instance-based learning includes nearest neighbor , locally weighted regression and case-based reasoning methods. Instance-based methods are sometimes referred to as lazy learning methods because they delay processing until a new instance must be classified. A key advantage of lazy learning is that instead of estimating the target function once for the entire instance space, these methods can estimate it locally and differently for each new instance to be classified.
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k-Nearest Neighbor Learning k-Nearest Neighbor Learning algorithm assumes all instances correspond to points in the n-dimensional space R n The nearest neighbors of an instance are defined in terms of Euclidean distance. Euclidean distance between the instances x i = <x i1 ,…,x in > and x j = <x j1 ,…,x jn > are: For a given query instance x q , f(x q ) is calculated the function values of k-nearest neighbor of x q = - = n r jr ir j i x x x x d 1 2 ) ( ) , (
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lec05-InstanceBased - INSTANCE-BASE LEARNING Instance-based...

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