UC知道请求高人翻译下英语
来源：百度知道 编辑：UC知道 时间：2024/09/22 01:53:52
Text categorization refers to the task of assigning
the pre-defined classes to text documents based on their
content. k-NN algorithm is one of top performing classifiers
on text data. However, there is little research work on
the use of different voting methods over text data. Also,
when a huge number of training data is available online,
the response speed slows down, since a test document has to
obtain the distance with each training data. On the other hand,
min–max-modular k-NN (M3-k-NN) has been applied to
large-scale text categorization. M3-k-NN achieves a good
performance and has faster response speed in a parallel computing
environment. In this paper, we investigate five different
voting methods for k-NN and M3-k-NN. The
experimental results and analysis show that the Gaussian
voting method can achieve the best performance among all
voting methods for both k-NN and M3-k-NN. In addition,M3-k-NN uses less k
the pre-defined classes to text documents based on their
content. k-NN algorithm is one of top performing classifiers
on text data. However, there is little research work on
the use of different voting methods over text data. Also,
when a huge number of training data is available online,
the response speed slows down, since a test document has to
obtain the distance with each training data. On the other hand,
min–max-modular k-NN (M3-k-NN) has been applied to
large-scale text categorization. M3-k-NN achieves a good
performance and has faster response speed in a parallel computing
environment. In this paper, we investigate five different
voting methods for k-NN and M3-k-NN. The
experimental results and analysis show that the Gaussian
voting method can achieve the best performance among all
voting methods for both k-NN and M3-k-NN. In addition,M3-k-NN uses less k
文本分类是指分配的任务
在预先确定的类别，以文本文件的基础上内容。的K -神经网络算法是一种效果最好的分类
对文本数据。然而，很少有研究工作
使用不同的投票方式的文字资料。也，
当大量的培训资料可在网上查阅，的响应速度变慢，因为一个测试文件，以
获得距离每个训练数据。另一方面，
民最大模块化的K -神经网络（立方米钾网络）已应用于大规模文本分类。 M3的钾神经网络实现了良好的
性能和更快的反应速度的并行计算环境。在本文中，我们调查五个不同
投票方法的K -神经网络和M3钾网络。那个
实验结果和分析表明，高斯
投票方法可以实现最佳的性能在所有
投票方法为的K -神经网络和M3钾网络。此外， M3的钾网络使用较少的K值，以实现更好的性能比的K -神经网络，从而为快的K -神经网络的并行计算
环境
我是按一个一个单词翻译的,没按句子翻,所以可能有点看不懂,但不过你说"大概",我也就……