What can I do with the Computer Manual

• Classify two dimensional, two class, data using a graphic user interface.
• 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 parameters 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/without discriminant functions
• Deterministic Boltzmann classifier
• Discrete Bayes classifier
• Expectation-maximization (EM)
• Genetic algorithm / programming
• Gibbs algorithm
• ID3
• Interactive Learning
• Linear Least squares / LMS
• Local polynomial fitting / boosting
• LVQ1 / LVQ3
• Marginalization / Maximum likelihood
• Multivariate adaptive regression splines
• Nearest Neighbors / Editing
• Optimal brain surgeon / Parzen windows / PNN
• Projection pursuit / Recurrent / Radial basis function networks
• Reduced coulomb energy algorithm (RCE) / RDA
• Store-Grabbag / Stumps / SVM / Voted perceptron

Clustering algorithms

• Agglomerative clustering (ADDC / AGHC)
• BIMSEC / Basic leader-follower clustering
• Competitive learning / Deterministic annealing (DA)
• Distinction sensitive learning vector quantization (DSLVQ)
• k-Means / Kohonen SOFM / Fuzzy k-means
• Fishers' linear discriminant / LVQ1 / LVQ3
• Minimum spanning tree / PCA (KLA) / Stepwise optimal hierarchical clustering (SOHC)
• Stochastic 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 / PCA)
• Information-based selection