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--- ece4580:module_recognition [2017/04/08 17:01] – [Module #2: Alternative Classifiers] typos
+++ ece4580:module_recognition [2024/08/20 21:38] (current) – external edit 127.0.0.1
@@ Line 92: / Line 92: @@
-__**Week #2: Kernel Trick**__ \\
+__**Week #3: Kernel Trick**__ \\
   - So far we've applied SVM on our dataset to distinguish car and face. We are going to learn more about SVM to see how it works.
   - If you played with the nice SVM GUI [[https://www.csie.ntu.edu.tw/~cjlin/libsvm/|here]] by generating some points in different colors, you can observe that the separating hyperplanes are not always a straight line. Why is it?
@@ Line 106: / Line 106: @@
   - Train SVM as you did before on this 4_9_3000 dataset with linear kernel. Compare the results with different kernels.
   - Train SVM on your car and face dataset with linear kernel. Compare the results with different kernels. Which dataset has more improvements with different kernels. Why?
+__**Week #4: Cross Validation**__ \\
+  - Besides kernel, another important thing to apply SVM correctly is to select good hyper-parameters.
+  - If you checked SVM GUI [[https://www.csie.ntu.edu.tw/~cjlin/libsvm/|here]], you might notice that different C might lead to different performance.
+  - Please quickly review the SVM material again, and explain what is C parameter here?
+  - One way to select good hyper-parameter is to apply "cross validation".
+  - Please read carefully about [[https://www.cs.cmu.edu/~schneide/tut5/node42.html|Cross Validation]]. The idea is mainly to leave some data untouched, and use it to test your selected parameter. And repeat this step using different untouched portion in your training data.
+  - Now, apply cross validation by using 10% of your data for cross validation. Try it on mnist_49_3000. Test C = 10^-2, 10^-1, 1, 10^1, 10^2, which one gives you best performance?
+  - What is the difference between K-fold and LOO(leave one out)?
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 [[ECE4580:Modules|ECE4580 Learning Modules]]
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