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java.lang.Objectgov.sandia.cognition.util.AbstractCloneableSerializable
gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner<InputOutputPair<? extends InputType,OutputType>,ResultType>
gov.sandia.cognition.learning.algorithm.AbstractSupervisedBatchAndIncrementalLearner<Vectorizable,Boolean,DiagonalConfidenceWeightedBinaryCategorizer>
gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation
@PublicationReference(author={"Koby Crammer","Mark Dredze","Fernando Pereira"}, title="Exact Convex Confidence-Weighted Learning", year=2008, type=Conference, publication="Advances in Neural Information Processing Systems", url="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.169.3364") public class ConfidenceWeightedDiagonalDeviation
An implementation of the Standard Deviation (Stdev) algorithm for learning a confidence-weighted categorizer. It updates only the diagonal of the covariance matrix, thus computing the variance for each dimension. This corresponds to the "Stdev-drop" version.
Field Summary | |
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protected double |
confidence
The confidence to use for updating. |
static double |
DEFAULT_CONFIDENCE
The default confidence is 0.85. |
static double |
DEFAULT_DEFAULT_VARIANCE
The default variance is 1.0. |
protected double |
defaultVariance
The default variance, which the diagonal of the covariance matrix is initialized to. |
protected double |
epsilon
Epsilon is the cached value 1 + phi^2. |
protected double |
phi
Phi is the standard score computed from the confidence. |
protected double |
psi
Psi is the cached value 1 + phi^2 / 2. |
Constructor Summary | |
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ConfidenceWeightedDiagonalDeviation()
Creates a new ConfidenceWeightedDiagonalVariance with default
parameters. |
|
ConfidenceWeightedDiagonalDeviation(double confidence,
double defaultVariance)
Creates a new ConfidenceWeightedDiagonalVariance with the given
parameters. |
Method Summary | |
---|---|
DiagonalConfidenceWeightedBinaryCategorizer |
createInitialLearnedObject()
Creates a new initial learned object, before any data is given. |
double |
getConfidence()
Gets the confidence to use for updating. |
double |
getDefaultVariance()
Gets the default variance, which the diagonal of the covariance matrix is initialized to. |
void |
setConfidence(double confidence)
Gets the confidence to use for updating. |
void |
setDefaultVariance(double defaultVariance)
Sets the default variance, which the diagonal of the covariance matrix is initialized to. |
void |
update(DiagonalConfidenceWeightedBinaryCategorizer target,
Vector input,
boolean label)
Updates the target using the given input and associated label. |
void |
update(DiagonalConfidenceWeightedBinaryCategorizer target,
Vectorizable input,
Boolean output)
The update method updates an object of ResultType using
the given a new supervised input-output pair, using some form of
"learning" algorithm. |
Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractSupervisedBatchAndIncrementalLearner |
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update |
Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner |
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clone, learn, learn, update |
Methods inherited from class java.lang.Object |
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equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchAndIncrementalLearner |
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learn |
Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner |
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learn |
Methods inherited from interface gov.sandia.cognition.learning.algorithm.IncrementalLearner |
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update |
Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable |
---|
clone |
Field Detail |
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public static final double DEFAULT_CONFIDENCE
public static final double DEFAULT_DEFAULT_VARIANCE
protected double confidence
protected double defaultVariance
protected double phi
protected double psi
protected double epsilon
Constructor Detail |
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public ConfidenceWeightedDiagonalDeviation()
ConfidenceWeightedDiagonalVariance
with default
parameters.
public ConfidenceWeightedDiagonalDeviation(double confidence, double defaultVariance)
ConfidenceWeightedDiagonalVariance
with the given
parameters.
confidence
- The confidence to use. Must be in [0, 1].defaultVariance
- The default value to initialize the covariance matrix to.Method Detail |
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public DiagonalConfidenceWeightedBinaryCategorizer createInitialLearnedObject()
IncrementalLearner
public void update(DiagonalConfidenceWeightedBinaryCategorizer target, Vectorizable input, Boolean output)
SupervisedIncrementalLearner
update
method updates an object of ResultType
using
the given a new supervised input-output pair, using some form of
"learning" algorithm.
target
- The object to update.input
- The supervised input to learn from.output
- The supervised output to learn from.public void update(DiagonalConfidenceWeightedBinaryCategorizer target, Vector input, boolean label)
target
- The target to update.input
- The supervised input value.label
- The output label associated with the input.public double getConfidence()
public void setConfidence(double confidence)
confidence
- The confidence. Must be between 0.5 and 1, inclusive.public double getDefaultVariance()
public void setDefaultVariance(double defaultVariance)
defaultVariance
- The default variance. Must be positive.
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