BriefGPT.xyz
Mar, 2020
构建和解释深度相似性模型
Building and Interpreting Deep Similarity Models
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Oliver Eberle, Jochen Büttner, Florian Kräutli, Klaus-Robert Müller, Matteo Valleriani...
TL;DR
提出了一种名为 BiLRP 的可解释聚类算法,通过将相似性得分分解为输入特征,使相似性得分可解释,可以应用于训练模型和数字人文领域的历史文献相似度评估。
Abstract
Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on the concept of 'distance' or '
similarity
'. Before similarities are used for
training
an actual m
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