AN INCREMENTAL HYBRID LEARNING ALGORITHM WITH NOISE RESISTANCE ABILITY

In the paper here, an incremental hybrid learning algorithm IHMCAP is proposed, which has the ability of noise resistance. This algorithm successfully combines probability based symbolic learning with neural learning. By adopting the FTART neural network proposed before, the IHMCAP not only proportions the learning accuracy between symbolic and neural parts, but also lays the two different thought levels aboard. The unique incremental learning mechanism employed enables the IHMCAP performs only one round learning to cover new training patterns. Moreover, it also depresses the noise sensibility of the learning system, which makes IHMCAP fit for tasks that require real time online learning.