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Statistical classification is the problem of identifying the sub-population to which new observations belong, where the identity of the sub-population is unknown, on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a variable behavior which can be studied by statistics.

1 vote

Problem training neural network for binary classification

There seems to be a big gap between the error on the training-set and the error on the validation-set. This points to overfitting. That would also explain the increase in error on the validation set w …
dimpol's user avatar
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2 votes

Various matrix as examples in neural network

So, the question you are asking is: "How do I tell the network that the values at the same position $(i,j)$ in different examples are related"? Dontloo is answering the question: "How do I tell the ne …
dimpol's user avatar
  • 1,042
1 vote
Accepted

Classification: one of the classes have a much wider range of predictor values then others.

But regarding the classes, you seem to have 4 classes and care about the distinction between each pair of classes, so using classification with 4 classes seems like the right choice. …
dimpol's user avatar
  • 1,042
1 vote

How to Implement a Neural Network for **Multidimensional** Input Classification?

Well, the whole idea of machine learning is that you let the algorithm decide which inputs are important and which aren't. At times, we might want to restrict the algorithm to focus on combining input …
dimpol's user avatar
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4 votes
1 answer
190 views

How to deal with 'near miss' labels in categorization?

I describe the general problem first, and then add an example for context: I'm working on a binary classifier using supervised learning. I have a set of examples with corresponding features and each …