Modifier and Type | Class and Description |
---|---|
class |
Propagation
Implements basic functionality that is needed by each of the propagation
methods.
|
Modifier and Type | Class and Description |
---|---|
class |
Backpropagation
This class implements a backpropagation training algorithm for feed forward
neural networks.
|
Modifier and Type | Class and Description |
---|---|
class |
ManhattanPropagation
One problem that the backpropagation technique has is that the magnitude of
the partial derivative may be calculated too large or too small.
|
Modifier and Type | Class and Description |
---|---|
class |
QuickPropagation
QPROP is an efficient training method that is based on Newton's Method.
|
Modifier and Type | Class and Description |
---|---|
class |
ResilientPropagation
One problem with the backpropagation algorithm is that the magnitude of the
partial derivative is usually too large or too small.
|
Modifier and Type | Class and Description |
---|---|
class |
ScaledConjugateGradient
This is a training class that makes use of scaled conjugate gradient methods.
|
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