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|Title: ||A Novel Self-Creating Neural Network for Learning Vector Quantization|
|Authors: ||Jung-Hua Wang|
|Issue Date: ||2018-11-02
|Publisher: ||Neural Processing Letters|
|Abstract: ||Abstract: This paper presents a novel self-creating neural network scheme which employs two resource counters to record network learning activity. The proposed scheme not only achieves the biologically plausible learning property, but it also harmonizes equi-error and equi-probable criteria. The training process is smooth and incremental: it not only avoids the stability-and-plasticity dilemma, but also overcomes the dead-node problem and the deficiency of local minimum. Comparison studies on learning vector quantization involving stationary and non-stationary, structured and non-structured inputs demonstrate that the proposed scheme outperforms other competitive networks in terms of quantization error, learning speed, and codeword search efficiency.|
|Relation: ||11(2) pp.139-151|
|Appears in Collections:||[電機工程學系] 博碩士論文|
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