From the webpage:
A major design goal of this portion of the library is to provide a highly modular and simple architecture for dealing with kernel algorithms. Towards this end, dlib takes a generic programming approach using C++ templates. In particular, each algorithm is parameterized to allow a user to supply either one of the predefined dlib kernels (e.g. RBF operating on column vectors), or a new user defined kernel. Moreover, the implementations of the algorithms are totally separated from the data on which they operate. This makes the dlib implementation generic enough to operate on any kind of data, be it column vectors, images, or some other form of structured data. All that is necessary is an appropriate kernel.
New features in 18.3:
- Machine Learning:
- Added the svr_linear_trainer, a tool for solving large scale support vector
regression problems.- Added a tool for working with BIO and BILOU style sequence taggers/segmenters. This is the new sequence_segmenter object and its associated structural_sequence_segmentation_trainer object.
- Added a python interface to some of the machine learning tools. These include the svm_c_trainer, svm_c_linear_trainer, svm_rank_trainer, and structural_sequence_segmentation_trainer objects as well as the cca() routine.
- Added point_transform_projective and find_projective_transform().
- Added a function for numerically integrating arbitrary functions, this is the new integrate_function_adapt_simpson() routine which was contributed by Steve Taylor
- Added jet(), a routine for coloring images with the jet color scheme.
This looks interesting. Lots of good references, etc.
I first saw this in a tweet by Mxlearn.