| Class | Description |
|---|---|
| FLDA |
Builds Fisher's Linear Discriminant function.
|
| GaussianProcesses |
* Implements Gaussian processes for regression without
hyperparameter-tuning.
|
| LDA |
Generates an LDA model.
|
| LinearRegression |
Class for using linear regression for prediction.
|
| Logistic |
Class for building and using a multinomial logistic regression model with a ridge estimator.
|
| MultilayerPerceptron |
A Classifier that uses backpropagation to classify instances.
|
| QDA |
Generates a QDA.
|
| SGD |
Implements stochastic gradient descent for learning various linear
models (binary class SVM, binary class logistic regression, squared loss, Huber loss and
epsilon-insensitive loss linear regression).
|
| SGDText |
Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
|
| SGDText.Count | |
| SimpleLinearRegression |
Learns a simple linear regression model.
|
| SimpleLogistic |
Classifier for building linear logistic regression
models.
|
| SMO |
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
|
| SMOreg |
SMOreg implements the support vector machine for regression.
|
| VotedPerceptron |
Implementation of the voted perceptron algorithm by Freund and
Schapire.
|
| XNV |
Implements the XNV method for semi-supervised learning using a kernel function (default: RBFKernel).
|