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The liability-threshold model is a threshold model of categorical (usually binary) outcomes in which a large number of variables are summed to yield an overall 'liability' score; the observed outcome is determined by whether the latent score is smaller or larger than the threshold. The liability-threshold model is frequently employed in ...
The winnow algorithm [1] is a technique from machine learning for learning a linear classifier from labeled examples. It is very similar to the perceptron algorithm.However, the perceptron algorithm uses an additive weight-update scheme, while Winnow uses a multiplicative scheme that allows it to perform much better when many dimensions are irrelevant (hence its name winnow).
Double descent in statistics and machine learning is the phenomenon where a model with a small number of parameters and a model with an extremely large number of parameters both have a small training error, but a model whose number of parameters is about the same as the number of data points used to train the model will have a much greater test ...
In the statistical learning theory framework, an algorithm is a strategy for choosing a function: given a training set = {(,), …, (,)} of inputs and their labels (the labels are usually ). Regularization strategies avoid overfitting by choosing a function that fits the data, but is not too complex.
A classification model (classifier or diagnosis [7]) is a mapping of instances between certain classes/groups.Because the classifier or diagnosis result can be an arbitrary real value (continuous output), the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure).
hists is a 2D-histogram of grayscale value and neighborhood average grayscale value pair. total is the number of pairs in the given image.it is determined by the number of the bins of 2D-histogram at each direction. threshold is the threshold obtained.
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Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]