lect13

lect13 - Lecture outline Classification Nave Bayes...

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Lecture outline Classification Naïve Bayes classifier Nearest-neighbor classifier
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Eager vs Lazy learners Eager learners: learn the model as soon as the training data becomes available Lazy learners: delay model-building until testing data needs to be classified Rote classifier: memorizes the entire training data
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k-nearest neighbor classifiers X X X (a) 1-nearest neighbor (b) 2-nearest neighbor (c) 3-nearest neighbor k -nearest neighbors of a record x are data points that have the k smallest distance to x
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k -nearest neighbor classification Given a data record x find its k closest points Closeness: Euclidean, Hamming, Jaccard distance Determine the class of x based on the classes in the neighbor list Majority vote Weigh the vote according to distance e.g., weight factor, w = 1/d 2
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Characteristics of nearest- neighbor classifiers Instance of instance-based learning No model building (lazy learners) Lazy learners: computational time in classification Eager learners: computational time in model
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This document was uploaded on 10/05/2010.

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lect13 - Lecture outline Classification Nave Bayes...

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