Metrics Space and Norm: Taxonomy to Distance Metrics
نویسندگان
چکیده
A lot of machine learning algorithms, including clustering methods such as K-nearest neighbor (KNN), highly depend on the distance metrics to understand data pattern well and make right decision based data. In recent years, studies show that can significantly improve performance or deep model in clustering, classification, recovery tasks, etc. this article, we provide a survey widely used challenges associated with field. The most current conducted area are commonly influenced by Siamese triplet networks utilized associations between samples while employing mutual weights metric (DML). They successful because their ability recognize relationships among similarity. Furthermore, sampling strategy, suitable metric, network structure complex difficult factors for researchers performance. So, article is significant it detailed which these components comprehensively examined valued whole, evidenced assessing numerical findings techniques.
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ژورنال
عنوان ژورنال: Scientific Programming
سال: 2022
ISSN: ['1058-9244', '1875-919X']
DOI: https://doi.org/10.1155/2022/1911345