本文提出了一种基于分类矢量量化器的小目标红外图像的压缩方法。
This paper presents a small target infrared image compression scheme based on classified vector quantization.
矢量量化码书设计本质是搜索训练矢量的最佳分类。
VQ codebook design is essentially a classification of training vectors.
针对这些问题，基于最小化学习误差的增量思想，该文将学习型矢量量化（LVQ）和生长型神经气（GNG）结合起来提出一种新的增量学习型矢量量化方法，并将其应用到文本分类中。
To solve these problems, based on minimizing the increment of learning errors and combining LVQ and GNG, the authors propose a new growing LVQ method and apply it to text classification.
本文基于学习矢量量化（LVQ）神经网络分类器，实现了舌象分析中的舌色、苔色自动分类。
Tongue color automatic classification, based on LVQ neural networks classifier, is proposed in this paper.
比较而言，学习矢量量化网络在分类能力和训练成本方面更胜一筹，而概率神经网络则在计算负载和易用性方面更好一些。
By comparison, LVQ network was better than the others in classification ability and training cost, and PNN network in computation load and easy use.
比较而言，学习矢量量化网络和概率神经网络在分类能力方面要比反向传播网络好一些，概率神经网络在计算负载方面比学习矢量量化网络要更胜一筹。
By comparison, LVQ network and PNN network are better than BPN network in classification ability, and PNN network is better than the others in computation load.
该文指出图像数据分类对矢量量化技术进行图像压缩编码的重要性。
This paper presents the importance of source classification to image data compression with vector quantization technique. Also, an image data classifying method is given.
该文指出图像数据分类对矢量量化技术进行图像压缩编码的重要性。
This paper presents the importance of source classification to image data compression with vector quantization technique. Also, an image data classifying method is given.
应用推荐