Search Results for "data capture framework"
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Showing 13 open source projects for "data capture framework"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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UnBBayes
Framework & GUI for Bayes Nets and other probabilistic models.
UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy. -
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Oryx
Lambda architecture on Apache Spark, Apache Kafka for real-time
Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large-scale machine learning. It is a framework for building applications but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering. The application is written in Java, using Apache Spark, Hadoop, Tomcat, Kafka, Zookeeper and more. Configuration uses a single Typesafe Config config file, wherein... -
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JCLALtext
Text processing module for JCLAL
JCLALtext is a class library designed to extend the framework JCLAL text tasks. JCLALtext is free, open source and developed with the Java programming language. JCLALtext is distributed under the GNU license. The researcher can use the class library by adding it to your project. -
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Chordalysis
Log-linear analysis (data modelling) for high-dimensional data
===== Project moved to https://github.com/fpetitjean/Chordalysis ===== Log-linear analysis is the statistical method used to capture multi-way relationships between variables. However, due to its exponential nature, previous approaches did not allow scale-up to more than a dozen variables. We present here Chordalysis, a log-linear analysis method for big data. Chordalysis exploits recent discoveries in graph theory by representing complex models as compositions of triangular structures, also known as chordal graphs. ... -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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libVMR
VMR - machine learning library
libVMR is a class library written in Java which implements code generator for group method of data handling - GMDH. The library is intended for users, with machine learning skills. libVMR provides an effective framework for the research and development of data mining and predictive analytics. libVMR is based on the most popular neural network model with a higher generalization ability from kernel tricks - vector machine by Reshetov (VMR). -
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LPCforSOS is a machine learning framework with a special focus on structured output spaces and pairwise learning. It supports currently multiclass, ordinal, hierarchical, multi-label and label ranking classification settings.
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DocCO
Non-disjoint groupping of Documents based on word sequence approach
This is a GUI for learning non disjoint groups of documents based on Weka machine learning framework. It offers the possibility to make non disjoint clustering of documents using both vectorial and sequential representation (word sequence approach based on WSK kernel). All data format supported by WEKA could be used in DocCO. Data could be loaded from files, from databases or from specified URL. All the preprocessing techniques implemented in WEKA could be used before performing the learning. -
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AdPreqFr4SL
Adaptive Prequential Learning Framework
The AdPreqFr4SL learning framework for Bayesian Network Classifiers is designed to handle the cost / performance trade-off and cope with concept drift. Our strategy for incorporating new data is based on bias management and gradual adaptation. Starting with the simple Naive Bayes, we scale up the complexity by gradually updating attributes and structure. Since updating the structure is a costly task, we use new data to primarily adapt the parameters and only if this is really necessary, do we adapt the structure. ... -
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MACSY
Modular Architecture for Cognitive Systems
Macsy is a framework for developing modular agents. Data is organised in blackboards. Computations are performed by modules that annotate the data in the blackboards. Modules communicate indirectly through the annotations that they leave in the blackboards. The framework enables the development of decentralised software agents for a plethora of applications. -
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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Program to performing the complete cycle of neural networks analysis: preparing data, choosing neural network (CasCor, MP, LogRegression, PNN), learning of network, monitoring learning state, ROC-analysis, optimization of network parameters using GA.
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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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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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