This library was originally implemented for the DIKU course Statistical Methods for Machine Learning. It contains the following algorithms:
- K nearest neighbours
- Linear discriminant analysis
- Multilayer perceptron (with backpropagation)
- Naive bayes (Also supporting different kernel estimator, like Epanechnikov and Gauassian kernels)
- Data normalization (zero mean, unit variance)
- Data rescaling (data rescaled to lie between zero and one)
- Linear regression
- Linear regression with basis functions
- k-fold cross validation
- Test set validation
Note that this library relies on JAMA for matrix operations.
- Examples (and other documentation)
- K-means clustering
- Principal component analysis
--- 2014/07/01: v0.1
Main method included for solving XOR-problem to show example of use. This algorithm has been enhanced and included in my more general ML library.
This is a Java implementation of the AI algorithm k-nearest neighbours (KNN) used for classification.
Constructor: NearestNeighbour(ArrayList<DataEntry> dataset, int k).
Main method included to give an example of use.
This algorithm has been enhanced and included in my more general ML library.