sparse coding中文是什么意思

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中文翻译
造句

稀疏编码(技术，方法)

sparse adj. 1.(树木分布等)稀的；(交通车辆等)稀疏的。 2.(人口、毛发等)稀少的。 3.(雨量)稀缺的；瘦小的。 sparse beard 稀疏的胡须。 a country of sparse population 人口稀少的小国。 n. -sity ,-ly adv.,-ness n.
coding n. 编码；译成电码。
coding for 编码
a sparse beard 疏疏的几根胡子
copious - sparse 大量
data sparse 数据稀疏
sparse and scattered 稀疏零落
sparse array 稀疏数组
sparse bristle 秃头鬃毛笔
sparse camel 秃头驼毛笔
sparse concealment 稀疏隐蔽
sparse file 稀疏文件
sparse flat 秃头平笔
sparse foliage 叶子稀疏
sparse graph 稀疏图
sparse hair 稀疏的头发
sparse index 稀疏索引
sparse insertion 稀插法
sparse inverse 稀疏逆矩阵
sparse matrices 稀疏矩阵
sparse matrix 稀疏矩阵; 稀巯阵
sparse pubescence 稀疏毛类型; 稀疏茸毛
sparse rearing 薄饲
sparse seeding 稀播
sparse signals 稀疏信号

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例句与用法

Based on unsupervised learning, sparse coding is suitable to describe images with non-gaussian distribution and can get rid of the high order redundancy among the image pixels . since the basis function of sparse coding has build-in clustering property, it increases the inter-class variations of the features
稀疏编码是一种基于非监督学习的算法，它适合描述具有非高斯分布的数据对象，能够有效地消除图像象素点之间的冗余，并具有内在的聚类特性。
Based on unsupervised learning, sparse coding is suitable to describe images with non-gaussian distribution and can get rid of the high order redundancy among the image pixels . since the basis function of sparse coding has build-in clustering property, it increases the inter-class variations of the features
稀疏编码是一种基于非监督学习的算法，它适合描述具有非高斯分布的数据对象，能够有效地消除图像象素点之间的冗余，并具有内在的聚类特性。
Based on the clustering property of the basis function of sparse coding, a basis function initialization method using fuzzy c mean algorithm is proposed to help the energy function of sparse coding to converge to a better local minimum for recognition . experimental results show that the classification and the sparseness of the features are both improved
经过模糊c均值聚类初始化后的基函数能够让稀疏编码的能量函数收敛到一个更有利于识别的局部最小点，试验结果表明特征的分类性和稀疏性都得到了提高。
Based on the clustering property of the basis function of sparse coding, a basis function initialization method using fuzzy c mean algorithm is proposed to help the energy function of sparse coding to converge to a better local minimum for recognition . experimental results show that the classification and the sparseness of the features are both improved
经过模糊c均值聚类初始化后的基函数能够让稀疏编码的能量函数收敛到一个更有利于识别的局部最小点，试验结果表明特征的分类性和稀疏性都得到了提高。
The class labels of the training samples are introduced during the training of the basis function to constrain the intra-class variations of the features . the features produced by the new sparse coding have large inter-class variations and small intra-class variations, thus the recognition performance of the reinforcement learning based sparse coding is better than that of traditional sparse coding
在基函数的训练过程中，通过引入训练样本的类别信息来限制特征类内距离的增加，用这类方法获得的特征既有较大的类间距离，又有较小的类内距离，识别性能得到了较大的提高。
The class labels of the training samples are introduced during the training of the basis function to constrain the intra-class variations of the features . the features produced by the new sparse coding have large inter-class variations and small intra-class variations, thus the recognition performance of the reinforcement learning based sparse coding is better than that of traditional sparse coding
在基函数的训练过程中，通过引入训练样本的类别信息来限制特征类内距离的增加，用这类方法获得的特征既有较大的类间距离，又有较小的类内距离，识别性能得到了较大的提高。
The class labels of the training samples are introduced during the training of the basis function to constrain the intra-class variations of the features . the features produced by the new sparse coding have large inter-class variations and small intra-class variations, thus the recognition performance of the reinforcement learning based sparse coding is better than that of traditional sparse coding
在基函数的训练过程中，通过引入训练样本的类别信息来限制特征类内距离的增加，用这类方法获得的特征既有较大的类间距离，又有较小的类内距离，识别性能得到了较大的提高。