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on (and not using a typedef, begin to see the typedef Statement). Here is an illustration of defining an easy framework for Keeping the
e. on the order of one thousand or fewer since the algorithm is cubic in the amount of attributes.). In order to utilize a nonlinear kernel then you should utilize the krr_trainer. This object is capable of mechanically estimating its regularization parameter applying depart-a person-out cross-validation.
This functionality computes the modularity of a selected graph clustering. This is the number that lets you know how very good the clustering is. Especially, it is the evaluate optimized because of the newman_cluster schedule.
This can be a benefit functionality for making roc_trainer_type objects that happen to be set up to select a point on the ROC curve with regard into the -1 class.
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This item signifies a function that will take a data sample and tasks it into kernel aspect House. The result is an actual valued column vector that represents some extent in the kernel feature space. Cases of this object are made utilizing the empirical_kernel_map.
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void* pointer which can be used with goto. See The goto Statement. Given a memory address saved within a pointer, you can use the indirection
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This perform performs the dimensionality lessening Edition of linear discriminant Evaluation. That is definitely, you give it a set of labeled vectors and it returns a linear transform that maps the enter vectors into a new space that may be fantastic for distinguishing between the various classes.
Note that if you want to make use of the linear kernel then you'll be far better off using the rls item as it really is optimized for this scenario.
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