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Bayesian kernel projection classifier (BKPC) is a nonlinear multicategory classifier which performs the classification of the projections of the data to the principal axes of the feature space. A Gibbs sampler is implemented to find the posterior distributions of the parameters.
The main function is bkpc.
The data can be passed to the bkpc function as:
a matrix of features,
a kernel matrix of either:
class ‘kern’ (a Gaussian kernel computed using the gaussKern{BKPC} function) or
class ‘kernelMatrix’ from library kernlab. This allows for a wider selection of inbuilt kernel generating functions as well as user defined functions.
The package contains a microarray dataset and a function to extract the marginal relevance of each feature for classification.
See ?bkpc and ?marginalRelevance
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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