2007/10/31 by Yen-Jen Oyang, Yen‐Jen Oyang, Chien-Yu Chen +7
Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Face and Expression Recognition #Machine Learning (stat.ML) #Machine Learning and Data Classification #Neural Networks and Applications #Statistics Theory (math.ST) #math.ST #stat.ML #stat.TH
paper · pdf · doi:10.48550/arxiv.0710.5896
Inclusion of a new "Remarks" section
openalex publication_date 2007/10/31 · arxiv created 2007/11/06 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This article proposes a novel density estimation based algorithm for carrying out supervised machine learning. The proposed algorithm features O(n) time complexity for generating a classifier, where n is the number of sampling instances in the training dataset. This feature is highly desirable in contemporary applications that involve large and still growing databases. In comparison with the kernel density estimation based approaches, the mathe-matical fundamental behind the proposed algorithm is not based on the assump-tion that the number of training instances approaches infinite. As a result, a classifier generated with the proposed algorithm may deliver higher prediction accuracy than the kernel density estimation based classifier in some cases.