Applying the LTSA architecture in practice is one of the important characteristics of the EPSS system.
系统最大的特点是将LTSA体系结构应用于实际。
As an effective manifold-learning method, the local tangent space alignment (LTSA) algorithm is sensitive to outliers.
局部切空间排列(LTSA)算法是一种有效的流形学习方法,但该算法对孤立点的存在非常敏感。
In order to enhance the robustness of LTSA algorithm, an outlier detection method based on the improved distance is presented in this paper.
基于编辑距离和多种后处理的生物医学文献实体名识别方法通过“全称缩写对识别算法”扩充词典,利用编辑距离算法提高识别召回率。
The paper focuses on the sensitivity of local tangent space alignment (LTSA) to outliers, and presents a robust local tangent space alignment (RLTSA) based on outlier detection.
研究局部切空间排列方法(LTSA)对离群点的敏感性,提出一种基于离群点检测的鲁棒局部切空间排列方法(RLTSA)。
The paper focuses on the sensitivity of local tangent space alignment (LTSA) to outliers, and presents a robust local tangent space alignment (RLTSA) based on outlier detection.
研究局部切空间排列方法(LTSA)对离群点的敏感性,提出一种基于离群点检测的鲁棒局部切空间排列方法(RLTSA)。
应用推荐