Competitive learning algorithms founded on adaptivity and sensitivity deletion methods
This paper describes two competitive learning algorithms from the viewpoint of deleting mechanisms of weight (reference) vectors. The techniques are termed the adaptivity and sensitivity deletions participated in the criteria of partition error and distortion error, respectively. Experimental results show the effectiveness of the proposed approaches in the average age distortion.
competitive learning deleting mechanism neural networks vector quantization