We propose a long-term memory design for artificial general intelligence based on Solomonoff’s incremental machine learning methods. We introduce four synergistic update algorithms that use a Stochastic Context-Free Grammar as a guiding probability distribution of programs. The update algorithms accomplish adjusting production probabilities, re-using previous solutions, learning programming idi...

This paper intends to establish theft Trojans detection system capable of adaptive dynamic feedback learning. To achieve this goal, this paper first studies the characteristics of the network data stream and theft Trojans communication data stream, then introduces support vector machine algorithm based on incremental learning, proposes the construction method of incremental learning samples, de...

Online metric learning has been widely exploited for large-scale data classification due to the low computational cost. However, amongst online practical scenarios where features are evolving (e.g., some vanished and new augmented), most models cannot be successfully applied these scenarios, although they can tackle instances efficiently. To address challenge, we develop a Evolving Metri...

Every second, thousands of credit or debit card transactions are processed in financial institutions. This extensive amount data and its sequential nature make the problem fraud detection particularly challenging. Most analytical strategies used production still based on batch learning, which is inadequate for two reasons: Models quickly become outdated require sensitive storage. The evolving b...

Adaptive networks include a set of nodes with adaptation and learning abilities for modeling various types of self-organized and complex activities encountered in the real world. This paper presents the effect of heterogeneously distributed incremental LMS algorithm with ideal links on the quality of unknown parameter estimation. In heterogeneous adaptive networks, a fraction of the nodes, defi...