## 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
graphic interface.
View how clustering algorithms progress through their training.
Make nice figures of your results...
## List of algorithms

### Classification algorithms

Ada Boost.
Backpropagation neural network (Five variants)
Balanced Winnow.
Perceptron (Several variants)
Relaxation (Several variants)
Bayesian Model Comparison
C4.5.
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.
Expectation-maximization (EM).
Genetic algorithm.
Genetic programming.
Gibbs algorithm.
ID3.
Interactive Learning (Learning with queries).
Linear Least squares (LS).
Least-mean squares (LMS).
Local polynomial fitting.
Local boosting
LVQ1.
LVQ3.
Marginalization
Maximum likelihood (ML).
Maximum likelihood model comparison (ML2).
Multivariate adaptive regression splines.
Nearest Neighbors.
Nearest Neighbor Editing.
Normal density discriminant function (NDDF).
Optimal brain surgeon.
Parzen windows.
Probabilistic neural network.
Projection pursuit regression.
Recurrent neural network
Radial basis function network
Reduced coulomb energy algorithm (RCE).
Regularized descriminant analysis (RDA).
Store-Grabbag.
Stumps.
Support-vector machines (SVM).
Voted perceptron.
### Clustering algorithms

Agglomerative clustering (ADDC).
Agglomerative hierarchical clustering algorithm (AGHC).
Basic iterative MSE clustering (BIMSEC).
Basic leader-follower clustering.
Competitive learning.
Deterministic annealing (DA)
Distinction sensitive learning vector quantization (DSLVQ).
k-Means.
Kohonen self-organizing feature maps (Kohonen_SOFM).
Fuzzy k-means.
Fishers' linear discriminant.
LVQ1
LVQ3
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).
Information-based selection.