perform time-consuming machine learning tasks within mlf in parallel; without making the handling complex.
Explore different models in parallel.
Use several CPUs to assess the quality of a particular model.
Time-Series
Build models on time series data using all available algorithms.
Kernel Methods
Gaussian process regression: build non-linear regression models in the kernel way.
Support vector machines: build classification models in the kernel way.
Supervised Analysis
Ridge Regression: Regression with built-in feature selection.
Additive regression and Boosting: refining models incrementally.
Quadratic regression models: build non-linear models which select the right features.
Neural networks: build highly accurate models for prediction tasks.
Unsupervised Analysis
Self-organizing maps: create two-dimensional plots of high dimensional data sets, preprocess large and noisy data sets, recall (one or more) missing values in the data.
fuzzy c-means: creates a fuzzy segmentation of the data.
Ward clustering: a crisp, agglomerative clustering method.
Learning vector quantization.
Fuzzy logic
Fuzzy decision trees: FS-ID3, a fuzzy variant of the ID3 learning algorithm to create decision trees.
Fuzzy rule generation: FS-FOIL, a fuzzy variant of Quinlan's FOIL method.
Cluster descriptions: FS-MINER, a proprietary method to find cluster descriptions.
Optimization of fuzzy controllers: RENO, a proprietary method, which uses numerical optimization to find computationally accurate and robust fuzzy rules.
Different types of fuzzy sets, t-norms and inference (Mamdani, Sugeno, Tagaki-Sugeno-Kang).
