Research of Semi-supervised Regression Algorithm Based on Density Distributio
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摘要: 提出了推导密度函数的基本假设,对密度函数进行了推导,通过密度函数实现了密度区域的划分;对同一密度范围内的未标签值标记的估计给出了具体的处理方法;最后介绍了基于密度分布的半监督回归算法的具体实现步骤。该算法实现了对未标签点的标记,能够减小对未标签点标签值的估计误差,提高估计的准确度。Abstract: The paper proposed a basic hypothesis for deducing density function and derived density function. It achieved density regional division through the density function, gave a specific estimating approach for computing tag value of untagged label within the same density range, and described specific implementation steps of semi-supervised regression algorithm based on density distribution. The algorithm achieves mark of untagged points, and can reduce estimating error of the tag value and improve estimation accuracy.
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