Compare the performance for different distance metrics

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Reference no: EM131755181

Assignment Description

Wine recognition data is given. Its number of features are:
class 1: 59, class 2: 71, class 3: 48.

NOTE: 1st dimension out of 15 identifies training and test split (1-training, 2-test). 2 nd dimension is class identifier (1-3).

Question 1. Distance Metrics Perform nearest neighbour classification experiments according to standard practices in pattern recognition. Use classification error as a fraction of incorrectly classified test points to compare different metrics from the course.

Question 2. K-means clustering Employ K-means to reduce the complexity of nearest neighbour classifier and compare the performance for different distance metrics.

Question 3. Neural Network Using Matlab Neural Network toolbox create a network, train and test with the wine data.

Attachment:- wine.data.rar

Reference no: EM131755181

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12/7/2017 7:00:52 AM

Wine recognition data is given. Its number of features are: class 1: 59, class 2: 71, class 3: 48. I would be requiring the matlab code and images for all 3 questions Assignment Description Wine recognition data is given. Its number of features are: class 1: 59, class 2: 71, class 3: 48. NOTE: 1st dimension out of 15 identifies training and test split (1-training, 2-test). 2 nd dimension is class identifier (1-3). Neural Network Using Matlab Neural Network toolbox create a network, train and test with the wine data.

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