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Topic > Machine Learning

Bayesian Methods (3 project)
Boosting (7 project)
Genetic Algorithms (2 project)
Model Selection and Validation (9 project)
Neural Networks (2 project)
Random Forests (3 project)
Recursive Partitioning (7 project)
Regularized and Shrinkage Methods (11 project)
Support Vector Machines (1 project)

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5 projects in result set.

0. Forecasting with Artificial Intelligence - The package ForAI uses machine learning methods such as artificial neural networks, support vector regression, extreme learning machines, and evolutionary algorithms for forecasting and prediction.

Development Status : 3 - Alpha [Filter]
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License : OSI Approved : GNU General Public License (GPL) (Now Filtering)
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Programming Language : R [Filter]
Topic : Machine Learning [Filter]
Topic : Optimization [Filter]
Topic : Regression Models [Filter]

Activity Percentile: 0.00

Registered: 2012-01-22 07:45

1. Conditional Random Fields - This project provides a set of tools for conditional random fields tasks such as decoding/inference/sampling/training.

Development Status : 4 - Beta [Filter]
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License : OSI Approved : GNU General Public License (GPL) (Now Filtering)
Natural Language : English [Filter]
Operating System : OS Independent [Filter]
Programming Language : C/C\+\+ [Filter]
Programming Language : R [Filter]
Topic : Graphical Models [Filter]
Topic : Machine Learning [Filter]

Activity Percentile: 0.00

Registered: 2010-12-28 07:13

2. Species Distribution Modelling - sdm is an extendable framework to develop species distributions models using individual and community-based approaches, generate ensembles of models, evaluate the models, and predict species potential distributions in space and time.

Development Status : 4 - Beta [Filter]
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Natural Language : English [Filter]
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Programming Language : R [Filter]
Topic : Machine Learning [Filter]
Topic : Spatial Data & Statistics : Ecological Analysis [Filter]

Activity Percentile: 0.00

Registered: 2014-05-26 16:09

3. Mixed-pair mutual information estimators - Fast calculation mutual information for comparisons between all types of variables including continuous vs continuous, continuous vs discrete and discrete vs discrete. Also provides jackknife bias correction and tests for association.

Development Status : 4 - Beta [Filter]
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Programming Language : Fortran [Filter]
Programming Language : R [Filter]
Topic : Bioinformatics [Filter]
Topic : High Performance Computing [Filter]
Topic : Machine Learning [Filter]

Activity Percentile: 0.00

Registered: 2012-08-10 08:48

4. Learn user preferences - Active machine learning methods are used to guess user intent in interactive R sessions, leading to more sensible, user-specific defaults for various functions.

Development Status : 1 - Planning [Filter]
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License : OSI Approved : GNU General Public License (GPL) (Now Filtering)
Natural Language : English [Filter]
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Programming Language : R [Filter]
Topic : Machine Learning [Filter]

Registered: 2010-11-26 16:34