?'S ON 5 STEPS OF SVM-KNN
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1. How do we know that the the training examples involved in the SVM classification (i.e. the neighbors found by doing the preliminary K-NN) lie closely to the decision boundary?? (referencing section 2, the paragraph before they list the 5 steps of SVM-KNN)

2. Step 2 of SVM-KNN computes the "accurate" distance function on the K_sl SAMPLES... (are these called samples because we have pruned the neighbors using the crude distance in step 1, there is no random sampling going on, right?

3. I don't understand step 3, how can you compute the distance of a union of neighbors?

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?'S ON KERNELIZING THE DISTANCE
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4. I am unsure of how valid it is when they simply add the absolute value of eigenvalue of kernel matrix to the diagonal of the kernel matrix to make it positive-definite.. it seems so sketchy, yet I am still getting familiar with the kernel matrix, and all the operations with it =).. plus, I don't know what it means to strengthen self-similarity? How can something become more similar to itself?

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QUESTION ON RESULTS COMMENT
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5. Paragraph 3 of 4.1.. What does it mean that a number of workarounds were needed to compute the shape context based distance?

Thank you! =)
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