The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
현재 상위권-k 오류 비율은 다중 범주 분류의 정확성을 측정하는 주요 방법 중 하나입니다. 맨 위-k 멀티클래스 SVM은 최상위 수준을 기반으로 경험적 위험을 최소화하도록 설계되었습니다.k 오류 비율. 최상위 학습을 위해 두 가지 SDCA 기반 알고리즘이 존재합니다.k SVM은 둘 다 최적화를 달성하기 위한 몇 가지 바람직한 속성을 가지고 있습니다. 그러나 두 알고리즘 모두 이론적 불완전성으로 인해 대부분의 경우 최적의 수렴을 달성할 수 없다는 심각한 단점이 있습니다. 수치 시뮬레이션을 통해 입증된 바와 같이, 수정된 SDCA 알고리즘을 적용하면 기존 두 가지 SDCA 기반 알고리즘의 실패와 달리 항상 최적의 수렴이 달성됩니다. 마지막으로 기존 알고리즘의 중요성을 명확히 하기 위해 분석 결과를 제시합니다.
Yoshihiro HIROHASHI
DENSO CORPORATION
Tsuyoshi KATO
Gunma University
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Yoshihiro HIROHASHI, Tsuyoshi KATO, "Corrected Stochastic Dual Coordinate Ascent for Top-k SVM" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 11, pp. 2323-2331, November 2020, doi: 10.1587/transinf.2019EDP7261.
Abstract: Currently, the top-k error ratio is one of the primary methods to measure the accuracy of multi-category classification. Top-k multiclass SVM was designed to minimize the empirical risk based on the top-k error ratio. Two SDCA-based algorithms exist for learning the top-k SVM, both of which have several desirable properties for achieving optimization. However, both algorithms suffer from a serious disadvantage, that is, they cannot attain the optimal convergence in most cases owing to their theoretical imperfections. As demonstrated through numerical simulations, if the modified SDCA algorithm is employed, optimal convergence is always achieved, in contrast to the failure of the two existing SDCA-based algorithms. Finally, our analytical results are presented to clarify the significance of these existing algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7261/_p
부
@ARTICLE{e103-d_11_2323,
author={Yoshihiro HIROHASHI, Tsuyoshi KATO, },
journal={IEICE TRANSACTIONS on Information},
title={Corrected Stochastic Dual Coordinate Ascent for Top-k SVM},
year={2020},
volume={E103-D},
number={11},
pages={2323-2331},
abstract={Currently, the top-k error ratio is one of the primary methods to measure the accuracy of multi-category classification. Top-k multiclass SVM was designed to minimize the empirical risk based on the top-k error ratio. Two SDCA-based algorithms exist for learning the top-k SVM, both of which have several desirable properties for achieving optimization. However, both algorithms suffer from a serious disadvantage, that is, they cannot attain the optimal convergence in most cases owing to their theoretical imperfections. As demonstrated through numerical simulations, if the modified SDCA algorithm is employed, optimal convergence is always achieved, in contrast to the failure of the two existing SDCA-based algorithms. Finally, our analytical results are presented to clarify the significance of these existing algorithms.},
keywords={},
doi={10.1587/transinf.2019EDP7261},
ISSN={1745-1361},
month={November},}
부
TY - JOUR
TI - Corrected Stochastic Dual Coordinate Ascent for Top-k SVM
T2 - IEICE TRANSACTIONS on Information
SP - 2323
EP - 2331
AU - Yoshihiro HIROHASHI
AU - Tsuyoshi KATO
PY - 2020
DO - 10.1587/transinf.2019EDP7261
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2020
AB - Currently, the top-k error ratio is one of the primary methods to measure the accuracy of multi-category classification. Top-k multiclass SVM was designed to minimize the empirical risk based on the top-k error ratio. Two SDCA-based algorithms exist for learning the top-k SVM, both of which have several desirable properties for achieving optimization. However, both algorithms suffer from a serious disadvantage, that is, they cannot attain the optimal convergence in most cases owing to their theoretical imperfections. As demonstrated through numerical simulations, if the modified SDCA algorithm is employed, optimal convergence is always achieved, in contrast to the failure of the two existing SDCA-based algorithms. Finally, our analytical results are presented to clarify the significance of these existing algorithms.
ER -