Intelligent system on chip（ So C） which integrates multiple sensors has been widely applied in Internet of Things. However

considering data fusion of multiple sensors

traditional SVM single-classifier can’t directly support small sample incremental learning for sensor data stream. Aiming at above problem

put forward a kind of ensemble incremental algorithm based on Bagging-SVM

the algorithm supports incremental learning by combining original ensemble classifiers with the new ones which can reflect new information change of incremental data sets

while the new classifiers trained by the data sets which is extracted from incremental data sets by Bootstrap means.Experimental results show that the algorithm compared with the single-classifier can effectively decrease classification error

improve classification accuracy and has good generalization ability

which can smoothly meet requirements of intelligent sensor system for online learning based on small sample data flow.