Search Results for "training"

DenseBlocks is an unsupervised classification method for n-dimensional numerical data. It creates a multi-dimensional density representation of given training samples, defines clusters accordingly and returns this representation which can further be used to classify related data.

NDN Backprop Neural Net Trainer implements the backpropagation functionality subset of the open source NeuronDotNet object library in a generic user friendly application.

...It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. For example, in machine with 64GB, CRF# encodes model with more than 4.5 hundred million features quickly.