What can I do with the Computer Manual
Classify two dimensional, two class, data using a graphic user interface. This allows easy access to any of a large number
of classification algorithms, clustering methods, and dimentionality reduction algorithms.
Enjoy easy access to over 50 classification algorithms and over 20 clustering and feature reduction methods.
Compare the performance of several algorithms on your data.
Find the best parameters for running an algorithm on your data.
Generate your own datasets either by entering their parameters (For Gaussian and Uniform distributions) or through a
View how clustering algorithms progress through their training.
Make nice figures of your results...
List of algorithms
Backpropagation neural network (Five variants)
Perceptron (Several variants)
Relaxation (Several variants)
Bayesian Model Comparison
Cascade-Correlation type neural network.
Classification and regression trees (CART).
Component classifiers with descriminant functions.
Component classifiers without descriminant functions
Deterministic Boltzmann classifier.
Discrete Bayes classifier.
Interactive Learning (Learning with queries).
Linear Least squares (LS).
Least-mean squares (LMS).
Local polynomial fitting.
Maximum likelihood (ML).
Maximum likelihood model comparison (ML2).
Multivariate adaptive regression splines.
Nearest Neighbor Editing.
Normal density discriminant function (NDDF).
Optimal brain surgeon.
Probabilistic neural network.
Projection pursuit regression.
Recurrent neural network
Radial basis function network
Reduced coulomb energy algorithm (RCE).
Regularized descriminant analysis (RDA).
Support-vector machines (SVM).
Agglomerative clustering (ADDC).
Agglomerative hierarchical clustering algorithm (AGHC).
Basic iterative MSE clustering (BIMSEC).
Basic leader-follower clustering.
Deterministic annealing (DA)
Distinction sensitive learning vector quantization (DSLVQ).
Kohonen self-organizing feature maps (Kohonen_SOFM).
Fishers' linear discriminant.
Minimum spanning tree.
Principal component analysis (PCA), also known as Karhunen-Louve transform (KLA).
Stepwise optimal hierarchical clustering (SOHC).
Stochstic simulated annealing (SA).
Feature selection and feature reduction algorithms
Culling genetic algorithm.
Hierarchical dimensionality reduction (HDR).
Independent component analysis (ICA).
Multidimensional scaling (MDS).
Non-linear principle component analysis (NLPCA).
Principle component analysis (PCA).