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There are some interesting papers on the equivalence of different learning algorithms (e.g. decision trees, logistic regression, svm, FF nn's). When it comes do
by trapper 17y ago
There are some interesting papers on the equivalence of different learning algorithms (e.g. decision trees, logistic regression, svm, FF nn's). When it comes down to it, a lot of the "different" methods actually model the same thing in roughly the same way, meaning the only difference in the algorithm's boiled down to ability to interpret the algorithms internal rules (e.g. nn is black box, decision tree isn't) and training time.
No references handy unfortunately.
- roundsquare 17y agoReally? Makes sense I guess, but it would be great if you do run across any papers. So I guess the real art comes down to 1) Picking your learning algo 2) Some details with each method. For nn's - do you use sigmoid squashing, etc... For knn - what is your distance metric.
- trapper 17y agoYes, I was surprised as well but if you think about it, it does makes sense. I'll dig around and see if I can find the paper.