Statistics 503 Homework 4
Due: in class on Wednesday, Mar 30 2005
Background: Neural networks and support vector machines are computationally intensive methods for
fitting classifiers to data. This homework explores how these classifiers work with the music data. It is to
be done individually, with help from Dr Cook as necessary.
Purpose: To learn how to fit neural networks and support vector machines to data, and how to assess the
fit.
Exercises:
1. Use your previous subsetting of the music data into 2/3 training, 1/3 test data. List your row numbers
for training and test sets.
2. Compute a new variable LFreq, which is the median of LFreq1-LFreq15.
3. Fit a neural network classifier to your training set using variables LVar, LAve, LMax, LFEner, LFreq.
(a) To begin use size=0, so that you are fitting a regression model. Report the model, the training
error and the test error.
(b) Next fit several models, size=2, 5, 10. Run each size 10 times, and tabulate the results. Report
the final value, training error and test error for each of the fits, in a table like the following.
Trial
Ave SD
Size
1 2 3 4 5 6 7 8 9 10
2
final value
train error
test error
5
final value
train error
test error
10
final value
train error
test error
4. Build an SVM classifier for the training data. Write down the classification rule prescribed by SVM.
Report the training and test error. Report which tracks are unbounded and bounded support vectors.