Search Results for "performance" - Page 2
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Showing 47 open source projects for "performance"
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JASA allows researchers in agent-based computational economics to write high-performance trading simulations using a number of different auction protocols. The software also provides base classes for implementing simple adaptive trading agents.
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DJVU++
The DjVu complete solution,with OCR Technology(Arabic ,English).
...o DjVu++ supports multiple formats: Convert PDF document into DjVu format with smaller file size and the same performance. Convert DjVu into PDF format. Combine images to a single DjVu document. Perform OCR operations on multiple image formats. -
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BONESA is an open source, user-friendly interface for tuning the numerical parameters of metaheuristics. This package includes a multi-objective parameter tuning algorithm and visualizations of the performance landscape.
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JConstraints
Java Framework to implement solutions to solve constraints problems
...Just to name a few algorithms, the Framework can solve any constraints problems using: - Regular Backtracking; - A variety of Backjumping algorithms; - A variety of arc-consistency algorithms; - Local search algorithm; - Tabu search algorithm; - Maximize solutions: comming soon; - Minimize solutions: comming soon; The Framework also enables heuristic injections so that the developers can achieve greater performance of algorithms using their own heuristics. Version 1.0: Released! Version 1.1: Coming soon See the tutorial in the Wiki page to learn how to use this Framework. -
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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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JaCHMM
Java Conditioned Hidden Markov Model library
...It gives an implementation of the Viterbi, Forward-Backward, Baum-Welch and K-Means algorithms, all adapted for the CHMM. JaCHMM is based on the JaHMM and also designed to achieve reasonable performance without making the code unreadable. Consequently, it offers a good way of applying the Conditioned Hidden Markov Model in various tasks, e.g., for scientific or teaching purposes. -
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OpenCyc is the open source version of the Cyc(r) technology, the world's largest and most complete general knowledge base and reasoning engine. A new release with even more content, better performance, and improved reliability will be up shortly.
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cognity
A neural network library for Java.
Cognity is an object-oriented neural network library for Java. It's goal is to provide easy-to-use, high level architecture for neural network computations along with reasonable performance. -
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SweetOnionCCG2PTBConverter
A tool that converts CCGBank to PTB
Conversion between different grammar frameworks is of great importance to comparative performance analysis of the parsers developed on them. This tool can convert CCG derivations to PTB trees by using Max Entropy models as well as visualizing the tree graphs. The main technical innovation presented here is the effective conversion method which achieves a F score over 95%. -
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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Wikipedia Concept Association Map (WCAM) is new approach for textual knowledge representation and understanding. All concepts and associations are stored in a graph database for better performance and easy distribution.
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...It adopts a parsing strategy of its own: dynamic concurrent parsing -- which made it much stronger than LR or LL family CCs (YACC, ANTLR, etc), but still keep good performance.
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Aleph is both a multi-platform machine learning framework aimed at simplicity and performance, and a library of selected state-of-the-art algorithms.
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Librería java de redes neuronales: Sencilla y rápida. Java Artificial Neural Networks library: Easy to use and high performance.
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MutationFinder is a biomedical natural language processing (NLP) system for extracting mentions of point mutations from free text. MutationFinder achieves high performance (99% precision, 81% recall on blind test data) as an information extraction system
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Conrad is both a high performance Conditional Random Field engine which can be applied to a variety of machine learning problems and a specific set of models for gene prediction using semi-Markov CRFs.
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Self Organization of an adaptive learning framework for pervasive graph structures from grids, utilities to semantic governance. Business, performance and provisioning assurance are converged with EGA, ITIL, eTOM,SOX, HIPPA, CobiT and AC
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DrPangloss is a python implementation of a three operator genetic algorithm, complete with a java swing GUI for running the GA and visualising performance, generation by generation
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Simulation platform for automated stock exchange trading. It delivers statistics to analyse performance on historic data and allows comparison between trading strategies, that can be coded in Java.
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GLD is a traffic flow simulator which can be used to evaluate the performance of AI traffic light controller algorithms and colearning navigation algorithms for cars. It is a discrete simulation which is designed to be easy to expand.
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multi-agent approach to traffic control: -3D visualization -XML-input for traffic networks -simulation progress control -basic performance statistics -delegate MAS -BDI based vehicles -pluggable behavior modules -pluggable coordination modules
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videonetwork
A simulator for self-organizing video replication and more
...The transport path of content is exploited to replicate content and limited storage space is handled by clean-up mechanisms. We implemented a simulator to show the algorithm's performance and to test its behavior under real circumstances. It is easily extendable to support several new scenarios or changes to the algorithm (see replication and client models).
