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SAMPLING Bagging vs Pasting

One way to get a diverse set of classifiers is to use very different training algorithms,as just discussed. Another approach is to use the same training algorithm for everypredictor but to train them on different random subsets of the training set. Whensampling is performed with replacement, this method is called bagging(short for bootstrap aggregating). When sampling is performed without...

Fast uniform negative sampling for rating matrix

petamind

Sometimes, we want to reduce the training time by using a subset of a very large dataset while the negative samples outnumbers the positive ones, e.g. word embedding. Another situation when we deal with implicit data. In this case, we may need to populate new data for negative values. This post demonstrates how to generate data for training using uniform negative sampling. The data Originally...

MLP for implicit binary collaborative filtering

In this post, we demonstrate Keras implementation of the implicit collaborative filtering. We also introduce some techniques to improve the performance of the current model, including weight initialization, dynamic learning rate, early stopping callback etc. The implicit data For demonstration purposes, we use the dataset generated from negative samples using the technique mentioned in this post...

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