LNKnet: Neural Network, Machine-Learning, and Statistical Software for Pattern Classification
Richard P. Lippmann, Linda Kukolich, Elliot Singer · 1993
Abstract : Patterndassification and clustering algorithms are key components of modern information processing systems used to perform tasks such as speech and image recognition, printedcharacter recognition, medical diagnosis, fault detection, process control, and financial decision making. To simplifY the task of applying these types of algorithms in new application areas, we have developed LNKnet-a software package that provides access toinore than 20 patternclassification, clustering, and featureselection algorithms. Included are the most important algorithms from the fields of neural networks, statistics, machine learning, and artificial intelligence. The algorithms can be trained and tested on separate data or tested with automatic crossvalidation. LNKnet runs under the UNIX operating system and access to the different algorithms is provided through a graphical pointandclick user interface. Graphical outputs include twodimensional (2D) scatter and decisionregion plots and 1-D plots of data histograms, classifier outputs, and error rates during training. Parameters of trained classifiers are stored in files from which the parameters can be translated into source-code subroutines (written in the C programming language) that can then be embedded in a user application program. Lincoln Laboratory and other research laboratories have used LNKnet successfully for many diverse applications.