@PublicationReference(author={"Shai Shalev-Shwartz","Yoram Singer"}, title="A New Perspective on an Old Perceptron Algorithm", year=2005, type=Conference, publication="Conference on Learning Theory", pages={815,824}, url="http://www.springerlink.com/index/hr4hrbyajy0y8a7l.pdf") public class Ballseptron extends AbstractKernelizableBinaryCategorizerOnlineLearner
| Modifier and Type | Field and Description |
|---|---|
static double |
DEFAULT_RADIUS
The default radius is 0.1.
|
protected double |
radius
The radius enforced by the algorithm.
|
vectorFactory| Constructor and Description |
|---|
Ballseptron()
Creates a new
Ballseptron with default parameters. |
Ballseptron(double radius)
Creates a new
Ballseptron with the given radius. |
| Modifier and Type | Method and Description |
|---|---|
double |
getRadius()
Gets the radius parameter.
|
void |
setRadius(double radius)
Sets the radius parameter.
|
<InputType> |
update(DefaultKernelBinaryCategorizer<InputType> target,
InputType input,
boolean label)
Performs a kernel-based incremental update step on the given object
using the given supervised data.
|
void |
update(LinearBinaryCategorizer target,
Vector input,
boolean label)
The
update method updates an object of ResultType using
the given a new supervised input-output pair, using some form of
"learning" algorithm. |
createInitialLearnedObject, createKernelLearner, learn, update, update, updatecreateInitialLearnedObject, getVectorFactory, setVectorFactory, updateupdateclone, learn, learn, updateequals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitupdatelearnlearncreateInitialLearnedObject, update, updateclonepublic static final double DEFAULT_RADIUS
protected double radius
public Ballseptron()
Ballseptron with default parameters.public Ballseptron(double radius)
Ballseptron with the given radius.radius - The radius.public void update(LinearBinaryCategorizer target, Vector input, boolean label)
AbstractOnlineLinearBinaryCategorizerLearnerupdate method updates an object of ResultType using
the given a new supervised input-output pair, using some form of
"learning" algorithm.update in class AbstractOnlineLinearBinaryCategorizerLearnertarget - The object to update.input - The supervised input vector to learn from.label - The supervised output label to learn from.public <InputType> void update(DefaultKernelBinaryCategorizer<InputType> target, InputType input, boolean label)
KernelizableBinaryCategorizerOnlineLearnerInputType - The input type for supervised learning. Will be passed to the
kernel function.target - The target object to update.input - The supervised input value.label - The supervised output value (label).public double getRadius()
public void setRadius(double radius)
radius - The radius parameter. Must be positive.