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MLP can be used for both regression and classification. For both tasks, we need first initialize the MLP model by specifying the parameters.
Train the model
For training, the following things need to be specified:
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Parameter | Description |
training.max.iterations | The maximum number of iterations (a.k.a. epoch) for training. |
training.batch.size | As the mini-batch update is leveraged for training, this parameter specify how many training instances are used in one batch. |
convergence.check.interval | If this parameters is set, then the model will be checked every time when the iteration is a multiple of this parameter. If the convergence condition is satisfied, the training will terminate immediately. |
tasks | The number of concurrent tasks. |
Use the trained model
Once the model is trained and stored, it can be reused later.
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String modelPath = ...; // the location of the existing model DoubleVector features = ...; // the features of an instance SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork(modelPath); DoubleVector labels = ann.getOutput(instance); // the label evaluated by the model |
Two class learning problem
To be added...In machine learning, two class learning is a kind of supervised learning problem. Given the instances, the goal of the classifier is to classify them into two classes.
Example: XOR problem
To be added...
Multi class learning problem
To be added..In machine learning, multiclass or multinomial classification is the problem of classifying instances into more than two classes.
Example:
To be added...
Regression problem
To be added..From the machine learning perspective, regression problem can be considered as the classification problem where the class label is a continuous value.
Example: Predict the sunspot activity
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