Study on Feature Selection and Feature Deep Learning Model For Big Data

机译：大数据特征选择与特征深度学习模型研究

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In the era of big data, agricultural big data effectively mined in the agricultural cloud service platform helps to provide intelligent services for agricultural production and management^[1]. Two big data feature selection methods are proposed based on the presence or absence labels of big data and potential value of data is mined better by effectively utilizing big data feature learning technologies. Transformation of data from data with primitive rough extraction features to data with features with stronger separability and high-level semantic features is of great significance for target task learning.

机译：在大数据时代，有效利用农业云服务平台挖掘的农业大数据有助于为农业生产和管理提供智能服务。 ^{[1]
。提出了两种基于大数据存在与否的大数据特征选择方法，通过有效利用大数据特征学习技术更好地挖掘数据的潜在价值。将数据从具有原始粗略提取特征的数据转换为具有更强可分离性和高级语义特征的数据对于目标任务学习具有重要意义。}

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《International Conference on Smart City and Systems Engineering》|2018年|792-795|共4页
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Ping Yu; Hui Yan;
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关键词
Feature extraction; Data models; Big Data; Adaptation models; Deep learning; Convolution;

机译：特征提取;数据模型;大数据;适应模型;深度学习;卷积;

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Study on Feature Selection and Feature Deep Learning Model For Big Data

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