| 摘 要: 随着科技的发展，数据分类问题应用在生活的多个方面，然而在面对庞大的数据时，往往采用压缩过的 稀疏数据，这就为分类模型的发展带来了极大的挑战。为了提高稀疏数据分类的准确性和正确率，提出了基于稀疏逻 辑回归的链接神经网络模型,由此构建成可靠的分类模型。以两类数据作为研究对象，首先进行数据预处理，再提取出 数据特征对其进行分类。研究结果表明，分类模型不仅可以应用于稀疏数据，而且正确率较神经网络模型的结果有所提 升，手写字的正确率从90.1%提高到94.86%，声音分类的正确率从70.3%提高到74.4%，证实该模型有效。 |
| 关键词: 逻辑回归;稀疏性;神经网络;多分类 |
|
中图分类号: TP391
文献标识码: A
|
|
| Application of Link Model based on Sparse Logistic Regression in Classification Problem |
|
CHANG Yudi
|
( Zhejiang Sci -Tech University, Hangzhou 310018, China)
2330920634@qq.com
|
| Abstract: With the development of science and technology, data classification is applied in many aspects of life. However, when facing huge data, compressed sparse data is often used, which brings great challenges to the development of classification models. In order to improve the precision and accuracy of sparse data classification, this paper proposes a link neural network model based on sparse logistic regression, so to build a reliable classification model. Taking two types of data as research object, data is preprocessed first, and then data features are extracted to classify them. The research results show that the classification model proposed in this paper can not only be applied to sparse data, but the accuracy is improved compared with the results of the neural network model. Accuracy of handwriting has increased from 90.1% to 94.86%, and accuracy of sound classification has increased from 70.3% to 74.4%, which proves that the model is effective. |
| Keywords: logistic regression; sparsity; neural network; multi-classification |
