Search Results for "minimum spanning tree"

MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack

popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known...

Minimum spanning tree (MST) algorithms can generate multiple, equally-minimal, MSTs but MST programs typically report only one, arbitrarily chosen MST. Similarly, most MST programs do not provide statistical metrics to support the credibility of the MSTs that they estimate. MSTgold estimates the number of alternative MSTs, reports up to a user-determined number of those trees, reports a consensus tree, and implements a bootstrapping metric to assess the reliability of alternate MST solutions. ...

Satsuma is a graph library for .NET, written in C#. Satsuma is an easy-to-use and flexible library, which implements all the necessary graph structures, and the fastest possible graph algorithms. DOCUMENTATION: http://satsumagraph.sourceforge.net/ Satsuma is not a charting library. 'Graph' is used in the mathematical sense here (e.g. nodes connected with arcs). Satsuma is released under the zlib license. This license is very permissive and allows you to use Satsuma for both personal...

Adaboost extensions for cost-sentive classification CSExtension 1 CSExtension 2 CSExtension 3 CSExtension 4 CSExtension 5 AdaCost Boost CostBoost Uboost CostUBoost AdaBoostM1 Implementation of all the listed algorithms of the cluster "cost-sensitive classification". They are the meta algorithms which requires base algorithms e.g. Decision Tree Moreover, Voting criteria is also required e.g. Minimum expected cost criteria Input also requires to load an arff file and a cost matrix (sample arff and cost files are uploaded for users' reference) This extension uses weka for classification and generates the classification model along with confusion matrix. For given dataset and cost matrix

Numerically estimate an efficiency of code construction algorithms. Randomly generate n binary words of length m with minimum Hamming distance d and create a prefix code (= retrieval tree) from them. Based on PhD work.

A minimalistic CASE tool that will allow end-users to analyze their graphs (plain-old graphs, UML diagrams, ERDs, etc.) by applying various graph theory functions to them (such as a shortest path algorithm or a spanning tree algorithm.)