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When do duplicates/frequencies matter in classification? – Kyunghyun Cho

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an interesting urban legend or wisdom is that a classifier we train will work better on examples that appear more frequently in the training set than on those that are rare. that is, the existence of duplicates or near-duplicates in the training set affects the decision boundary learned by a classifier. for instance, imagine training a face detector for your phone’s camera in order to determine which filter (one optimized for portraits and the other for other types of pictures). if most of the training examples for building such face detector were taken in bright day light, one often without hesitation says that this face detector would work better on pictures taken under bright day light than on pictures taken indoor. this sounds pretty reasonable until you start thinking of some simplified scenarios. And, that started for me a few years ago, which eventually led me to write this blog post. so, let’s consider a very simple binary classification setup. let 𝐷 = { ( 𝑥 𝑛 , 𝑦 𝑛 ) }

an interesting urban legend or wisdom is that a classifier we train will work better on examples that appear more frequently in the training set than on those that are rare. that is, the existence of duplicates or near-duplicates in the training set affects the decision boundary learned by a classifier. for instance, imagine training a face detector for your phone’s camera in order to determine which filter (one optimized for portraits and the other for other types of pictures). if most of the training examples for building such face detector were taken in bright day light, one often wit

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