Incremental learning aims to alleviate the catastrophic forgetting problem of deep neural networks during sequential data stream. This is even more challenging when old unavailable, since system can only be trained under supervision current data. To address this problem, we proposed a prototype sampling mechanism based on K-means clustering method. On one hand, use pick out class-representative...

Inductive programming systems characteristically exhibit an exponential explosion in search time as one increases the size of the programs to be generated. As a way of overcoming this, we introduce incremental learning, a process in which an inductive programming system automatically modifies its inductive bias towards some domain through solving a sequence of gradually more difficult problems ...

This paper discusses aspects of consistency and generalization in connectionist networks which learn through incremental training by examples or rules. Differences between training set learning and incremental rule or example learning are presented. Generalization, the ability to output reasonable mappings when presented with novel input patterns, is discussed in light of the above learning met...

Proximal SVM (PSVM), which is a variation of standard SVM, leads to an extremely faster and simpler algorithm for generating a linear or nonlinear classifier than classical SVM. An efficient incremental method for linear PSVM classifier has been introduced, but it can’t apply to nonlinear PSVM and incremental technique is the base of online learning and large data set training. In this paper we...

In many practical situations in neural network learning, it is often expected to further improve the generalization capability after the learning process has been completed. One of the common approaches is to add training data to the neural network. In view of the learning methods of human beings, it seems natural to build posterior learning results upon prior results, which is generally referr...