Release Notes: This release added a tool for training histogram-of-oriented-gradient based object detectors and examples showing how to use this type of detector to perform real-time face detection. It also added multi-threaded training options for the multiclass classifiers as well as numerous other usability improvements.
Release Notes: This release added a tool for solving large scale support vector regression problems to the library as well as a structural SVM tool for learning BIO or BILOU style sequence tagging models. It also added Python interfaces to a number of dlib's machine learning tools.
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.