-recent trends and advanced variants of learning vector quantization for classification learning. J. Artif. Intell. Softw. Comput. Res. 7(1), 65-81 (2017)Villmann, T., Bohnsack, A., Kaden, M.: Can learning vector quantization be an alternative to SVM and deep learning? - recent trends...
the network has assigned the input vectorpto classk* and α2k*will be 1. Of course, this assignment can be a good one or a bad one, fortk*can be 1 or 0, depending on whether the input belonged to classk* or not.
经典的K-means聚类算法通过最小化数据点和最近邻中心的距离来寻找各个类中心。江湖中还有个别名,叫“矢量量化vector quantization”(这个在我的博客上也有提到)。我们可以把K-means当成是在构建一个字典D∊Rnxk,通过最小化重构误差,一个数据样本x(i)∊Rn可以通过这个字典映射为一个k维的码矢量。所以K-means实...
Learning vector quantization (LVQ) is an algorithm that is a type of artificial neural networks and uses neural computation. More broadly, it can be said to be a type of computational intelligence. This algorithm takes a competitive, winner-takes-all approach to learning and is also related to...
[ICML2015]Deep Learning with Limited Numerical Precision 2 聚类量化:Deep Compression 聚类量化来源于韩松ICLR2016的论文Deep Compression。聚类量化是就是把权重和梯度相近的值使用K-means聚类,然后将同类的数统一替换为与之相近的浮点数。聚类后权重字典对应的value保存量化后的权重值,字典的key保存量化值的索引。
Learning Vector Quantization (LVQ,学习矢量量化) Self-Organizing Map(SOM,自组织映射) Locally Weighted Learning(LWL,局部加权学习) 3. Regularization Algorithms(正则化算法) 正则化是对另一种方法(通常是回归方法)的扩展,使基于其复杂性的模型受到惩罚,支持更简单的模型,这些模型在泛化能力方面也比较好。
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Learning Vector Quantization The representation for LVQ is a collection of codebook vectors selected randomly in the beginning and adapted to best summarize the training dataset over various iterations of the learning algorithm. After learning, the codebook vectors can be used to make predictions just...
Courville, “Deep learning vector quantization,” in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2016.[205] C. Szegedy et al., “Intriguing properties of neural networks.”[206] G. Hinton et al., “Deep neural networks for acoustic modeling ...
(VQ-VAE) framework while reducing its computational complexity based on shape-gain vector quantization. In this method, the magnitude of the latent vector is quantized using a non-uniform scalar codebook with a proper transformation function, while the direction of the latent vector is quantized ...