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Title: Algorithms for Accelerating Machine Learning with Wide and Deep Models
Other Titles: Wide・Deepモデルを用いた機械学習を高速化するためのアルゴリズム
Authors: Ida, Yasutoshi
Author's alias: 井田, 安俊
Keywords: Machine Learning
Sparsity-Inducing Norms
Deep Learning
Feature Selection
Efficient Algorithm
Issue Date: 23-Mar-2021
Publisher: Kyoto University
Conferring University: 京都大学
Degree Level: 新制・課程博士
Degree Discipline: 博士(情報学)
Degree Report no.: 甲第23310号
Degree no.: 情博第746号
Conferral date: 2021-03-23
Degree Call no.: 新制||情||127(附属図書館)
Degree Affiliation: 京都大学大学院情報学研究科知能情報学専攻
Examination Committee members: (主査)教授 鹿島 久嗣, 教授 田中 利幸, 教授 山下 信雄
Provisions of the Ruling of Degree: 学位規則第4条第1項該当
Rights: 2章は同著者らによる論文1. Advances in Neural Information Processing Systems 32, 2019.と2. 人工知能学会論文誌, 36(1), A-JB1 1-11, 2021.に基づく。4章は同著者らによる論文1.Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017に基づく。Copyright (C) 2017 International Joint Conferences on Artificial Intelligence.URL: https://www.ijcai.org/。5章は同著者らによる論文1.International Joint Conference on Neural Networks, 2019.と2. 人工知能学会論文誌, 35(3), C-JA3 1-10, 2020.に基づく。
DOI: 10.14989/doctor.k23310
URI: http://hdl.handle.net/2433/263771
Appears in Collections:140 Doctoral Dissertation (Informatics)

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