@PublicationReference(title="Confidence-Weighted Linear Classification", author={"Mark Dredze","Koby Crammer","Fernando Pereira"}, year=2008, type=Conference, publication="International Conference on Machine Learning", url="http://portal.acm.org/citation.cfm?id=1390190") public class ConfidenceWeightedDiagonalVariance extends AbstractSupervisedBatchAndIncrementalLearner<Vectorizable,java.lang.Boolean,DiagonalConfidenceWeightedBinaryCategorizer>
Modifier and Type | Field and Description |
---|---|
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 |
phi
Phi is the standard score computed from the confidence.
|
Constructor and Description |
---|
ConfidenceWeightedDiagonalVariance()
Creates a new
ConfidenceWeightedDiagonalVariance with default
parameters. |
ConfidenceWeightedDiagonalVariance(double confidence,
double defaultVariance)
Creates a new
ConfidenceWeightedDiagonalVariance with the given
parameters. |
Modifier and Type | Method and Description |
---|---|
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,
java.lang.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. |
update
clone, learn, learn, update
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
learn
learn
update
clone
public static final double DEFAULT_CONFIDENCE
public static final double DEFAULT_DEFAULT_VARIANCE
protected double confidence
protected double defaultVariance
protected double phi
public ConfidenceWeightedDiagonalVariance()
ConfidenceWeightedDiagonalVariance
with default
parameters.public ConfidenceWeightedDiagonalVariance(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.public DiagonalConfidenceWeightedBinaryCategorizer createInitialLearnedObject()
IncrementalLearner
public void update(DiagonalConfidenceWeightedBinaryCategorizer target, Vectorizable input, java.lang.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 and 1, inclusive.public double getDefaultVariance()
public void setDefaultVariance(double defaultVariance)
defaultVariance
- The default variance. Must be positive.