Search Results for "gnu science" - Page 3
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Showing 60 open source projects for "gnu science"
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The Darwin Genetic Programming Environment is a graphical Genetic Programming Environment for the facilitation of research in Genetic Programming.
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Extensible framework that enables productive design, analysis, and execution of arbitrary-sized neural-networks or system on a distributed, scalable, high-throughput runtime platform. Enables synapse-oriented-programming.
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Highly reusable and extensible Decision-Tree (Max-Gain) framework comprising of comprehensive input-processing and display functionality. Handles nominal, linear, continuous data. For preliminary description, refer - http://sushain.com/blog/archives/
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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TrimGA is a lightweight genetic algorithm library written in pure Java 6.0 that can be quickly applied to most optimization problems.
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TimeSleuth - Temporal Rule Discovery
Temporal and Causal Decision Rules
TimeSleuth discovers temporal decision rules. It also judges the (a)causality of the rules. TimeSleuth can discover rules that involve time: {if (rainy_yesterday = true) then rainy_today = true}, or {if (rainy_tomorrow = true) then rainy_today = true}. -
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This project aimed at creating framework and binary data format for etymological Arabic system. and will not continue hosted at sourceforge because the term of use determine me as enemy, so I am prohibited from using sourceforge services.
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Supertagger
Software for assigning supertags.
Supertagging is a process of statistical lexical disambiguation, preprocessing step to parsing, which assigns LTAG tree categories to the lexical items present in the input sentence. Thus, if the input sentence is in the form of a dependency tree, the task of the supertagger is to assign the most probable TAG family to each node and edge in the dependency tree. -
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Data Mining Platform is a platform for data mining and analysis. It contains many of the new and sophisticated methods such as kernel-based classification, two-way clustering, bayesian networks, pattern recognition for time series analysis and many other
