Project Release Information
This release includes a large number of new minor features and usability improvements. It also includes a new machine learning tool for learning to rank objects. This is the dlib::svm_rank_trainer, an implementation of the well known SVM-Rank algorithm. Moreover, the implementation runs in O(n*log(n)) time and is therefore suitable for use with large training datasets.
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.
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.
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.
This release contains a number of new features and bugfixes. Some highlights are a structural support vector machine method for learning to detect objects in images, and two new general purpose tools for solving the MAP problem in graphical models.
dlib is a C++ library for developing portable applications dealing with networking, threads, graphical interfaces, data structures, linear algebra, machine learning, XML and text parsing, numerical optimization, Bayesian nets, and numerous other tasks.