Maximum margin learning with sub-SPNs for cell classification = 보조 합-곱 네트워크와 최대 마진 훈련을 이용한 세포 이미지 분류

This paper describes an algorithm using deep probabilistic model, referred to as sum-product networks (SPNs), for cell classification: it take a trained pathologist to distinguish the human epithelial type 2 cells with 73.3{\%} accuracy. The SPNs reduce generalization errors by maximizing the margin between the conditional probability of the true label and the maximum conditional probability of the label that is not a true label. In the SPNs architecture, the most confusing classes are grouped such that have a common parent sum node, referred to as sub-networks of SPNs (sub-SPNs). The sub-SPNs are one of the solutions to gradient diffusion problems, and are combined with maximum margin learning algorithm. The proposed SPN performed better than all other state-of-the-art algorithms on HEp-2 cells dataset and convolutional neural networks on Feulgen stained cells dataset.
Advisors
Yoo, Chang-Dongresearcher유창동researcher
Publisher
한국과학기술원
Issue Date
2015
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 미래자동차학제전공, 2015.2 ,[iv, 29 p. :]

Keywords

sum-product networks; sub-networks of SPNs; maximum margin learning; 합-곱 네트워크; 보조 네트워크; 최대 마진 훈련

URI
http://hdl.handle.net/10203/221385
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=657545&flag=t
Appears in Collection
PD-Theses_Master(석사논문)
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