All releases tagged feature additions
Release Notes: This release brings a number of new features to the library. Highlights include a probabilistic CKY parser, tools for creating applications using the Bulk Synchronous Parallel computing model, and two new clustering algorithms: Chinese Whispers and Newman's modularity clustering.
Release Notes: This release has focused on adding a set of graph cut algorithms. In particular, tools for finding the minimum weight cut on a graph, finding the MAP assignment of a Potts style Markov random field, and structural SVM tools for learning the parameters of such a Markov model have been added.
Release Notes: This release contains a number of new features, bugfixes, and usability improvements. Highlights include a structural support vector machine method for learning to solve assignment problems and new feature extractors for detecting objects in images.
Release Notes: This release contains a number of new features and bugfixes. Some highlights are a structural support vector machine method for learning to do sequence labeling, as well as a graph-based image segmentation tool.
Release Notes: This release enables dlib::pipe objects to be used for interprocess or network communication. It also adds the ability to distribute the work involved in optimizing a structural support vector machine across many networked computers and multi-core processors.
Release Notes: In addition to some minor bug fixes, this release adds a multiclass support vector machine, as well as a tool for solving the optimization problem associated with structural support vector machines.
Release Notes: The major new feature in this release is a general purpose trust region algorithm for performing non-linear optimization. It also adds the Levenberg-Marquardt algorithm for solving non-linear least squares problems.
Release Notes: In addition to many minor usability improvements, this release includes a new tool for performing kernel ridge regression on large datasets. It also implements an efficient method for computing leave-one-out cross-validation error rates. Finally, this release also includes a new example program detailing the steps necessary to create custom matrix expressions.
Release Notes: This release adds a few new algorithms to the library. The most important are implementations of the HOG image feature extractor, the OCA optimizer, and the OCAS linear support vector machine trainer. Support for loading and saving LIBSVM-formatted data files has also been added.
