1.9
  * New classifier: Naive Bayes
1.8
  * Self-Organized maps (SOM) implementation added.
  * Bug fixed on genetic algorithms data load
1.7
  * New neural network: Hopfield nets added
1.6.1
  * Hyperpipes bug fixed
  * Data set domains builder returns range for numeric attributes
1.6 
  * New classifier: Hyperpipes
  * New clusterer: Hierarchical Weighted Average Linkage
  * New clusterer: Hierarchical Centroid Linkage
  * New clusterer: Hierarchical Median Linkage
  * New clusterer: Hierarchical Ward's method Linkage 
  * New clusterer: Diana (Divisive Analysis)
  * Example code added: Simple website clusterer
  * New simple matching distance function added for discrete attribute vectors
1.5
  * ClassifierEvaluator class added: Ideal for experimentation with classifiers
  * New classifier: Multilayer Perceptron using Backpropagation neural network
  * New clusterer: Hierarchical Single Linkage
  * New clusterer: Hierarchical Complete Linkage
  * New clusterer: Hierarchical Average Linkage
  * Simple Statistics module added
  * Simple Proximity functions module added
  * New parameter for K-Means based clusterers: Custom centroid function
1.4 
  * Backpropagation neural networks rebuilt from zero: Faster, leaner code, more parameterizable. 
  * All algorithms include the Parameterizable module.
1.3
  * DataSet class added to wrap data used in classifiers
  * All classifiers use the new DataSet class
  * All classifiers extend Classifier class
  * Clustering algorithm implemented: K Means
  * Clustering algorithm implemented: Bisecting K Means
  * get_rules method returns generated rules, do not use to_s anymore
1.2  
  * New module organization
  * Added PRISM algorithm implementation
  * Added OneR algorithm implementation 
  * Added ZeroR algorithm implementation
  * Example list added to the forrest documentation
  * Backpropagation networks: Checking output dimensions during training
1.1
  * Backpropagation networks: Train method returns net error.
1.0
  * Initial release