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Research on Latent Factor Model and its Optimization Algorithms of Machine Learning

机译:机器学习潜在因子模型及其优化算法研究

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There are some bottleneck problems in the supervised machine learning and unsupervised machine learning. In view of the current problems, this paper tries to make some meaningful exploration. The main work is as follows: Research on the statistical analysis of factor analysis and latent variable and in some valuable research results of typical machine learning, and some no analysis method and factor analysis of supervised learning or hidden variables method to contact with the typical analysis, summary of the comprehensive characteristics of implicit factor model and to reveal the hiding data structures help and contributions.
机译:监督机器学习和无监督机器学习中有一些瓶颈问题。鉴于目前的问题,本文试图进行一些有意义的探索。主要工作如下:对因子分析和潜在变量的统计分析以及在典型机器学习的一些有价值的研究结果中的研究,以及对典型分析接触的监督学习或隐藏变量的分析方法和因子分析,隐式系数模型的综合特征概述,并揭示了隐藏数据结构的帮助和贡献。

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