Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as "mapped features" in feature nodes and the structure is expanded in wide sense in the "enhancement nodes." The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if…

• NN
  National Natural Science Foundation of China

  Award: 61572540
• FP
  Fundo para o Desenvolvimento das Ciências e da Tecnologia

  Award: 019/2015/A1