New notes on using ML to generalize from small amounts of training data: attacking MNIST with just 10 training examples for each digit. 93.81% accuracy: http://cognitivemedium.com/rmnist_anneal_ensemble …
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If I remember correctly I did get around 98.4% accuracy on the test set (without any preprocessing) with GMMs (20-25 Gaussians) using all the training data. SVM with rbf kernel was something like 98.6%. So it wasn't overfitting much.
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Yes thats why I said use each digit as a mean and choose a covariance. No training ver easy to form baseline classifier. What I meant was, as far as I remember, GMMs didn't perform that bad on MNIST.
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Well, having trained a whole bundle of baselines already, I'm not going to do another without a really compelling reason.
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