Hi,
I read a question that someone else asked here, but I couldn't find the question nor the answers (if any), so I will ask again, as I'm now working around with the classifier. I tried to apply the classifier to the task of handwritten recognition using the MNIST dataset. The best result I got was an overall accuracy of about 42% (by that I mean that after training the entire dataset, the proportion of right predictions from the first to the last training example was 42%), after playing a little with the encoders. Of course this is better than the expected 10% accuracy of a random picker algorithm, but it falls short of what is accomplished by other (linear) algorithms. For those interested, I attached a plot of the accuracy. So here comes the question: what are the inner workings of the classifier? I'm puzzled as it doesn't have a SP. Can someone help or point to some reading? Best regards, Allan
<<attachment: mnist_classifier.png>>
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