A super fast vector support classifier using novelty detection and no synaptic tuning
Radu Dogaru, Ioana Dogaru · 2016
A novel classifier architecture is introduced and its performances are evaluated against state of the art shallow classifiers. Its main advantage consists in a very fast learning ensured by a novelty detection algorithm, selecting a list of prototypes among the training samples, used as centers in a radial basis functions neurons layer. Only the radius of the basis functions is optimized to improve generalization in conjunction with an overlapping parameter and there is no need for synaptic tuning. Compared to state of the art models such as SVM (Support Vector Machine) or ELM (Extreme Learning Machine) our SFSVC (Super Fast Vector Support Classifier) it offers equal performance while having a more compact and fast algorithm. Thus SFSVC is well suited for embedded processing of big collections of data such as data from satellite remote sensing units, automotive sensors etc. with a good potential of being directly integrated into sensing platforms.