| MultiClassification Class |
Inheritance HierarchyNamespace: Imsl.DataMining.Neural
Assembly: ImslCS (in ImslCS.dll) Version: 6.5.2.0
SyntaxThe MultiClassification type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | MultiClassification |
Creates a classifier.
|
Methods| Name | Description | |
|---|---|---|
| ComputeStatistics |
Computes classification statistics for the supplied network patterns
and their associated classifications.
|
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) |
 | Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) |
| GetHashCode | Serves as a hash function for a particular type. (Inherited from Object.) |
| GetType | Gets the Type of the current instance. (Inherited from Object.) |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) |
| PredictedClass |
Calculates the classification probablities for the input pattern
x, and returns the class with the highest probability.
|
| Probabilities |
Returns classification probabilities for the input pattern x.
|
| ToString | Returns a string that represents the current object. (Inherited from Object.) |
| Train |
Trains the classification neural network using supplied training
patterns.
|
Properties| Name | Description | |
|---|---|---|
 | Error |
The error function for use by QuasiNewtonTrainer
for training a classification network.
|
| Network |
Returns the network being used for classification.
|
RemarksExtends neural network analysis to solving multi-classification problems. In these problems, the target output for the network is the probability that the pattern falls into each of several classes, where the number of classes is 3 or greater. These probabilities are then used to assign patterns to one of the target classes. Typical applications include determining the credit classification for a business (excellent, good, fair or poor), and determining which of three or more treatments a patient should receive based upon their physical, clinical and laboratory information. This class signals that network training will minimize the multi-classification cross-entropy error, and that network outputs are the probabilities that the pattern belongs to each of the target classes. These probabilities are scaled to sum to 1.0 using softmax activation.
See Also