Search Results for "machine learning workloads"
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Showing 8 open source projects for "machine learning workloads"
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The goal of this project is to investigate optimal ways to do genre classification for the ten indigenous South African languages. Funded by Dept of Arts and Culture of the SA Government. http://www.trifonius.co.za/projects/genre-classification
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VecText
Converting text to a structured representation
VecText is an application that converts raw text to a structured format suitable for various data mining software. The application is written in interpreted programming language Perl. A part of the functionality is realized by external modules (e.g., Lingua::Stem::Snowball for stemming). The graphical user interface enables user-friendly software employment without requiring specialized technical skills and knowledge of a particular programming language, names of libraries and their... -
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A machine learning-based method for somatic variant identification from low-pass next-generation sequencing.
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Part-of-speech tagging is the task of assigning symbols from a particular set to words in a natural language text. ACOPOST implements and extends well-known machine learning techniques and provides a uniform environment for testing.
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BreakDancer-0.0.1 is a Perl package that provides genome-wide detection of structural variants from next generation paired-end sequencing reads.
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BCAR is a library for the associative classification, which denotes "Boosting Class Association Rules". BCAR provides a general tool for classification tasks with various types of input data.
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openEAR is the Munich Open-Source Emotion and Affect Recognition Toolkit developed at the Technische Universität München (TUM). It provides efficient (audio) feature extraction algorithms implemented in C++, classfiers, and pre-trained models on well-known emotion databases. It is now maintained and supported by audEERING. Updates will follow soon.
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Open, extensible web-based collaborative platform for microarray gene expression, sequence and PPI data analysis, exposing distinct chainable components for clustering, pattern discovery, statistics (thru R), machine-learning algorithms and visualization
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