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* The **adatron** version of the perceptron described next. | * The **adatron** version of the perceptron described next. | ||
- | In each case make sure that your perceptron includes a bias term (in slide set 2 and page 7 in the book you will find guidance on how to add a bias term to an algorithm that is expressed without one). | + | In each case make sure that your implementation of the classifier includes a bias term (in slide set 2 and page 7 in the book you will find guidance on how to add a bias term to an algorithm that is expressed without one). |
=== The adatron === | === The adatron === | ||
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Here's what you need to do: | Here's what you need to do: | ||
- | - Implement the pocket algorithm and the adatron; each classifier should be implemented by a separate class, and use the same interface used in the code provided for the perceptron algorithm. | + | - Implement the pocket algorithm and the adatron; each classifier should be implemented by a separate class, and use the same interface used in the code provided for the perceptron algorithm. Make sure each classifier you use (including the original version of the perceptron) implements a bias term. |
- Compare the performance of these variants of the perceptron on the Gisette and QSAR datasets by computing an estimate of the out of sample error on a sample of the data that you reserve for testing (the test set). In each case reserve about 60% of the data for training, and 40% for testing. To gain more confidence in our error estimates, repeat this experiment using 10 random splits of the data into training/test sets. Report the average error and its standard deviation in a [[https://en.wikibooks.org/wiki/LaTeX/Tables|LaTex table]]. Is there a version of the perceptron that appears to perform better? (In answering this, consider the differences you observe in comparison to the standard deviation). | - Compare the performance of these variants of the perceptron on the Gisette and QSAR datasets by computing an estimate of the out of sample error on a sample of the data that you reserve for testing (the test set). In each case reserve about 60% of the data for training, and 40% for testing. To gain more confidence in our error estimates, repeat this experiment using 10 random splits of the data into training/test sets. Report the average error and its standard deviation in a [[https://en.wikibooks.org/wiki/LaTeX/Tables|LaTex table]]. Is there a version of the perceptron that appears to perform better? (In answering this, consider the differences you observe in comparison to the standard deviation). | ||