RSS Feed - BlogShowing Items 481-500 of 676 on page 25 of 34: First Previous 20 21 22 23 24 25 26 27 28 29 30 Next Last
About: Python module for machine learning multivariate time series Changes:Initial Announcement on mloss.org.
|
About: Ankus is an open source data mining / machine learning based MapReduce that supports a variety of advanced algorithms. Changes:Initial Announcement on mloss.org.
|
About: DIANNE is a modular software framework for designing, training and evaluating artificial neural networks on heterogeneous, distributed infrastructure . It is built on top of OSGi and AIOLOS and can transparently deploy and redeploy (parts of) a neural network on multiple machines, as well as scale up training on a compute cluster. Changes:Initial Announcement on mloss.org.
|
About: Estimates statistical significance of association between variables and their principal components (PCs). Changes:Initial Announcement on mloss.org.
|
About: ARTOS can be used to quickly learn models for visual object detection without having to collect a set of samples manually. To make this possible, it uses ImageNet, a large image database with more than 20,000 categories. Changes:Initial Announcement on mloss.org.
|
About: You should never compute, maintain, or update the inverse of a symmetric positive definite matrix if you do not have to. Computing the inverse or manipulating it is inherently instable. You can [...] Changes:Initial Announcement on mloss.org.
|
About: Kaiye Wang, Ran He, Wei Wang, Liang Wang, Tiuniu Tan. Learning Coupled Feature Spaces for Cross-modal Matching. In ICCV, 2013. Changes:Initial Announcement on mloss.org.
|
|
About: Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation. Changes:Initial Announcement on mloss.org.
|
About: Block-Coordinate Frank-Wolfe Optimization for Structural SVMs Changes:Initial Announcement on mloss.org.
|
About: This is a Matlab/C++ "toolbox" of code for learning and inference with graphical models. It is focused on parameter learning using marginalization in the high-treewidth setting. Changes:Initial Announcement on mloss.org.
|
About: Embarrassingly Parallel Array Computing: EPAC is a machine learning workflow builder. Changes:Initial Announcement on mloss.org.
|
About: Distributed optimization: Support Vector Machines and LASSO regression on distributed data Changes:Initial Upload
|
About: An annotated java framework for machine learning, aimed at making it really easy to access analytically functions. Changes:Now supports OLS and GLS regression and NaiveBayes classification
|
About: Classification and Regression Training in Parallel Using NetworkSpaces: Augment some caret functions using parallel processing Changes:Initial Announcement on mloss.org.
|
About: This provide a semi-supervised learning method based co-training for RGB-D object recognition. Besides, we evaluate four state-of-the-art feature learing method under the semi-supervised learning framework. Changes:Initial Announcement on mloss.org.
|
About: Learns dynamic network changes across conditions and visualize the results in Cytoscape. Changes:Initial Announcement on mloss.org.
|
About: A C++ Library for Discrete Graphical Models Changes:Initial Announcement on mloss.org.
|
About: KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) Java software tool that can be used for a large number of different knowledge data discovery tasks. KEEL provides a simple GUI based on data flow to design experiments with different datasets and computational intelligence algorithms (paying special attention to evolutionary algorithms) in order to assess the behavior of the algorithms. It contains a wide variety of classical knowledge extraction algorithms, preprocessing techniques (training set selection, feature selection, discretization, imputation methods for missing values, among others), computational intelligence based learning algorithms, hybrid models, statistical methodologies for contrasting experiments and so forth. It allows to perform a complete analysis of new computational intelligence proposals in comparison to existing ones. Moreover, KEEL has been designed with a two-fold goal: research and educational. KEEL is also coupled with KEEL-dataset: a webpage that aims at providing to the machine learning researchers a set of benchmarks to analyze the behavior of the learning methods. Concretely, it is possible to find benchmarks already formatted in KEEL format for classification (such as standard, multi instance or imbalanced data), semi-supervised classification, regression, time series and unsupervised learning. Also, a set of low quality data benchmarks is maintained in the repository. Changes:Initial Announcement on mloss.org.
|
About: This library implements the Optimum-Path Forest classifier for unsupervised and supervised learning. Changes:Initial Announcement on mloss.org.
|