Incremental knowledge acquisition and self learning from text
Daswin De Silva, Damminda Alahakoon · 2010
Incremental learning is a core necessity in developments towards intelligent machines. Artificial learning as implemented in contemporary neural network algorithms does not fully encompass an incremental, autonomous learning capacity. In this paper we present a self learning algorithm capable of incrementally acquiring knowledge across learning periods. A dynamic unsupervised learning algorithm, the GSOM algorithm, forms the basis of the presented incrementally knowledge acquiring self learning (IKASL) algorithm, to which we have introduced a layer of aggregation for continuous learning, knowledge acquisition and retention. We also present a novel application of the IKASL algorithm for continuous learning of hidden patterns from semantics of text.