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See:
Description
| Class Summary | |
| AnalyseNominal | This class was created to make it easy to train MaxEnt models from files in the same format that Weka uses. |
| BinaryFeature | This is used when only binary features are needed. |
| BinaryFeatures | |
| BinaryProblem | |
| ByteFeature | This class is used when we are sure the feature will have a value in 0-255 for each data pair |
| CGRunner | This class will call Conjugate Gradient on a LambdaSolve object to find optimal parameters, including imposing a Gaussian prior on those parameters. |
| Convert | This is used to convert an array of double into byte array which makes it possible to keep it more efficiently. |
| Data | |
| DataDouble | |
| DataGeneric | |
| DataString | |
| Experiment | An object of this class is an experiment (x,y) , a single training sample. |
| Experiments | This class represents the training samples. |
| Feature | This class is used as a base class for TaggerFeature for the tagging problem and for BinaryFeature for the general problem with binary features. |
| Features | |
| HoldDouble | This class is used hold one double value. |
| LambdaSolve | This is the main class that does the core computation in IIS. |
| LambdaSolveBinary | |
| Problem | This is a general class for Problem to be solved by the MaxEnt toolkit. |
| ProblemSolverHPSG | |
| ReadDataHPSG | |
| ReadDataHPSGBinary | |
| ReadDataWeka | |
| WekaProblemSolver | |
| WekaProblemSolverCombinations | |
This package contains an implementation of Improved Iterative Scaling. It has supporting data structures, such as Experiments and Features. To use iterative scaling, you should must create a LambdaSolve object and call ImprovedIterative() on it.
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