Self learning machines using Deep Networks

Self learning machines as defined in this paper are those learning by observation under limited supervision, and continuously adapt by observing the surrounding environment. The aim is to mimic the behavior of human brain learning from surroundings with limited supervision, and adapting its learning according to input sensory observations. Recently, Deep Belief Nets (DBNs) [1] have made good use of unsupervised learning as pre-training stage, which is equivalent to the observation stage in humans. However, they still need supervised training set to adjust the network parameters, as well as being nonadaptive to real world examples. In this paper, Self Learning Machine (SLM) is proposed based on deep belief networks and deep auto encoders.