Download JAR files tagged by spaces with all dependencies

An ensemble learning method inspired by bagging and random sub-spaces. Trains an ensemble of decision trees on random subspaces of the data, where each subspace has been transformed using principal components analysis.

The YUI Compressor is a JavaScript compressor which, in addition to removing comments and white-spaces, obfuscates local variables using the smallest possible variable name. This obfuscation is safe, even when using constructs such as 'eval' or 'with' (although the compression is not optimal is those cases) Compared to jsmin, the average savings is around 20%.

MASSIS is a framework that facilitates the simulation of scenarios with multiple agents (representing people, robots, sensors, etc.) in indoor environments (i.e., in a building). MASSIS provides support for designing spaces and specifying the behavior of the elements and agents in them It is possible to define a great diversity of behaviours, from a simple sensor to the decisions of a person. MASSIS has been designed to keep this flexibility withough hindering performance. The framework is capable of supporting thousands of agents, each one with an specific behavior.

The S-Space Package is a collection of algorithms for building Semantic Spaces as well as a highly-scalable library for designing new distributional semantics algorithms. Distributional algorithms process text corpora and represent the semantic for words as high dimensional feature vectors. This package also includes matrices, vectors, and numerous clustering algorithms. These approaches are known by many names, such as word spaces, semantic spaces, or distributed semantics and rest upon the Distributional Hypothesis: words that appear in similar contexts have similar meanings.

Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20) For more information see: Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.