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We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data with large size.
Modern, cross platform library of functions used in CoCo software
...Some functionality may also be useful in more general Motorola 6809 centered projects.
CocoCommons provides a set of libraries for common tasks such as reading DECB and RBF filesystems, interpretting common file formats, and displaying CoCo-like screens. These are used extensively in the DriveWire 4 project and the upcoming nineserver project. In time it these libraries should implement most of the functionality now found in the Toolshed project.