I don't get something in the fitting faces article, does an SVM kernel when doing logistic regression really exist? Isn't it just a linear kernel?

[adamhipster]

In https://www.auduno.com/2014/01/05/fitting-faces/ it states the following

in our case we implemented a logistic regression classifier with an SVM kernel (which is what the original paper suggests)

My knowledge on classification is limited. I know what SVMs are on a high level, I know what logistic regression is. But I didn't know what a kernel was. So I looked it up and learned what it more or less is (if I remember correctly it is a dot product that somehow equates to intersecting transposed data points in an arbitrary higher dimensional space). But in all those explanations the words "SVM kernel" do not exist, since support vector machines use of kernel themselves such as a linear or gaussian kernel, or you name it. I searched on via Google for quite a while.

So I took a look in the source code and the word "SVM kernel" does not seem to exist. I just queried on the word "kernel".

https://github.com/auduno/clmtools/search?utf8=%E2%9C%93&q=kernel

To me it seems that the implementation in actuality is a logistic regression classifier with a linear kernel.

My question is: is this the case? Or is there something inherent SVM-like about this particular kernel? When I look at Wikipedia, it does not mention SVM kernels: https://en.wikipedia.org/wiki/Support_vector_machine (it mentions linear kernels, etc.). But when I look at http://scikit-learn.org/stable/modules/svm.html it does mention SVM kernels but not in conjunction with logistic regression.

[auduno]

Hi, you're right, that was actually a wrong use of terms. The kernel is indeed a linear kernel, what I meant to say was that the classifier was trained using SVM methods. Thanks for the note, I'll correct that in the blog post!

[auduno]

This has now been fixed in the blog post.