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See:
Description
| Interface Summary | |
|---|---|
| CacheStaleIndicator | Indicates when the value/gradient during training becomes stale. |
| MaxLattice | The interface to classes implementing the Viterbi algorithm, finding the best sequence of states for a given input sequence. |
| SumLattice | |
| Transducer.Incrementor | Methods to be called by inference methods to indicate partial counts of sufficient statistics. |
| TransducerTrainer.ByOptimization | |
| Class Summary | |
|---|---|
| CRF | |
| CRF.Factors | A simple, transparent container to hold the parameters or sufficient statistics for the CRF. |
| CRF.State | |
| CRF.TransitionIterator | |
| CRFCacheStaleIndicator | Indicates when the value/gradient becomes stale based on updates to CRF's parameters. |
| CRFOptimizableByBatchLabelLikelihood | Implements label likelihood gradient computations for batches of data, can be easily parallelized. |
| CRFOptimizableByBatchLabelLikelihood.Factory | |
| CRFOptimizableByLabelLikelihood | An objective function for CRFs that is the label likelihood plus a Gaussian or hyperbolic prior on parameters. |
| CRFOptimizableByLabelLikelihood.Factory | |
| CRFTrainerByLabelLikelihood | In the future this class may go away in favor of some default version of CRFTrainerByValueGradients... |
| CRFTrainerByStochasticGradient | Trains CRF by stochastic gradient. |
| CRFTrainerByValueGradients | A CRF trainer that can combine multiple objective functions, each represented by a Optmizable.ByValueGradient. |
| CRFWriter | |
| FeatureTransducer | |
| HMM | Hidden Markov Model |
| HMM.State | |
| HMM.TransitionIterator | |
| InstanceAccuracyEvaluator | Reports the percentage of instances for which the entire predicted sequence was correct. |
| MaxLatticeDefault | Default, full dynamic programming version of the Viterbi "Max-(Product)-Lattice" algorithm. |
| MaxLatticeDefault.Factory | |
| MaxLatticeFactory | |
| MEMM | |
| MEMM.State | |
| MEMM.TransitionIterator | |
| MEMMTrainer | |
| MultiSegmentationEvaluator | |
| NoopTransducerTrainer | A TransducerTrainer that does no training, but simply acts as a container for a Transducer; for use in situations that require a TransducerTrainer, such as the TransducerEvaluator methods. |
| PerClassAccuracyEvaluator | |
| Segment | Represents a labelled chunk of a Sequence segmented by a
Transducer, usually corresponding to some object extracted
from an input Sequence. |
| SegmentationEvaluator | |
| ShallowTransducerTrainer | |
| SimpleTagger | This class's main method trains, tests, or runs a generic CRF-based sequence tagger. |
| SimpleTagger.SimpleTaggerSentence2FeatureVectorSequence | Converts an external encoding of a sequence of elements with binary
features to a FeatureVectorSequence. |
| SumLatticeBeam | |
| SumLatticeBeam.Factory | |
| SumLatticeConstrained | |
| SumLatticeDefault | Default, full dynamic programming implementation of the Forward-Backward "Sum-(Product)-Lattice" algorithm |
| SumLatticeDefault.Factory | |
| SumLatticeFactory | |
| ThreadedOptimizable | An adaptor for optimizables based on batch values/gradients. |
| TokenAccuracyEvaluator | Evaluates a transducer model based on predictions of individual tokens. |
| Transducer | |
| Transducer.State | |
| Transducer.TransitionIterator | |
| TransducerEvaluator | An abstract class to evaluate a transducer model. |
| TransducerTrainer | An abstract class to train and evaluate a transducer model. |
| TransducerTrainer.ByIncrements | |
| TransducerTrainer.ByInstanceIncrements | |
| ViterbiWriter | Prints the input instances along with the features and the true and predicted labels to a file. |
Transducers, including Conditional Random Fields (CRFs).
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