gov.sandia.cognition.learning.algorithm.perceptron
Interface LinearizableBinaryCategorizerOnlineLearner<InputType>

Type Parameters:
InputType - The input type that kernel learning happens on.
All Superinterfaces:
BatchAndIncrementalLearner<InputOutputPair<? extends InputType,Boolean>,DefaultKernelBinaryCategorizer<InputType>>, BatchLearner<Collection<? extends InputOutputPair<? extends InputType,Boolean>>,DefaultKernelBinaryCategorizer<InputType>>, Cloneable, CloneableSerializable, IncrementalLearner<InputOutputPair<? extends InputType,Boolean>,DefaultKernelBinaryCategorizer<InputType>>, Serializable, SupervisedBatchAndIncrementalLearner<InputType,Boolean,DefaultKernelBinaryCategorizer<InputType>>, SupervisedBatchLearner<InputType,Boolean,DefaultKernelBinaryCategorizer<InputType>>, SupervisedIncrementalLearner<InputType,Boolean,DefaultKernelBinaryCategorizer<InputType>>

public interface LinearizableBinaryCategorizerOnlineLearner<InputType>
extends SupervisedBatchAndIncrementalLearner<InputType,Boolean,DefaultKernelBinaryCategorizer<InputType>>

Interface for an online learner of a kernel binary categorizer that can also be used for learning a linear categorizer. Thus, there are a second set of methods that are similar to the one for the normal learner that are specifically for linear learning. The companion to this class is KernelizableBinaryCategorizerOnlineLearner.

Since:
3.3.0
Author:
Justin Basilico
See Also:
KernelizableBinaryCategorizerOnlineLearner

Method Summary
 LinearBinaryCategorizer createInitialLinearLearnedObject(VectorFactory<?> vectorFactory)
          Creates the initial learned object.
 SupervisedIncrementalLearner<Vectorizable,Boolean,LinearBinaryCategorizer> createLinearLearner(VectorFactory<?> vectorFactory)
          Creates a new linear learner using the standard learning interfaces based on this learner and its parameters.
 void update(LinearBinaryCategorizer target, InputOutputPair<? extends Vectorizable,Boolean> data, VectorFactory<?> vectorFactory)
          Performs a linear incremental update step on the given object using the given supervised data.
 void update(LinearBinaryCategorizer target, Iterable<? extends InputOutputPair<? extends Vectorizable,Boolean>> data, VectorFactory<?> vectorFactory)
          Performs a linear incremental update step on the given object using the given supervised data.
 void update(LinearBinaryCategorizer target, Vectorizable input, boolean output, VectorFactory<?> vectorFactory)
          Performs a linear incremental update step on the given object using the given supervised data.
 void update(LinearBinaryCategorizer target, Vectorizable input, Boolean output, VectorFactory<?> vectorFactory)
          Performs a linear incremental update step on the given object using the given supervised data.
 
Methods inherited from interface gov.sandia.cognition.learning.algorithm.SupervisedIncrementalLearner
update
 
Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchAndIncrementalLearner
learn
 
Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner
learn
 
Methods inherited from interface gov.sandia.cognition.learning.algorithm.IncrementalLearner
createInitialLearnedObject, update, update
 
Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable
clone
 

Method Detail

createInitialLinearLearnedObject

LinearBinaryCategorizer createInitialLinearLearnedObject(VectorFactory<?> vectorFactory)
Creates the initial learned object.

Parameters:
vectorFactory - The vector factory to use.
Returns:
A new linear binary categorizer.

update

void update(LinearBinaryCategorizer target,
            Iterable<? extends InputOutputPair<? extends Vectorizable,Boolean>> data,
            VectorFactory<?> vectorFactory)
Performs a linear incremental update step on the given object using the given supervised data.

Parameters:
target - The target object to update.
data - The supervised training data
vectorFactory - The vector factory to use.

update

void update(LinearBinaryCategorizer target,
            InputOutputPair<? extends Vectorizable,Boolean> data,
            VectorFactory<?> vectorFactory)
Performs a linear incremental update step on the given object using the given supervised data.

Parameters:
target - The target object to update.
data - The supervised training data
vectorFactory - The vector factory to use.

update

void update(LinearBinaryCategorizer target,
            Vectorizable input,
            Boolean output,
            VectorFactory<?> vectorFactory)
Performs a linear incremental update step on the given object using the given supervised data.

Parameters:
target - The target object to update.
input - The supervised input value.
output - The supervised output value (label).
vectorFactory - The vector factory to use.

update

void update(LinearBinaryCategorizer target,
            Vectorizable input,
            boolean output,
            VectorFactory<?> vectorFactory)
Performs a linear incremental update step on the given object using the given supervised data.

Parameters:
target - The target object to update.
input - The supervised input value.
output - The supervised output value (label).
vectorFactory - The vector factory to use.

createLinearLearner

SupervisedIncrementalLearner<Vectorizable,Boolean,LinearBinaryCategorizer> createLinearLearner(VectorFactory<?> vectorFactory)
Creates a new linear learner using the standard learning interfaces based on this learner and its parameters.

Parameters:
vectorFactory - The vector factory to use.
Returns:
A linear version of this learning algorithm.