JP7559943B2 - 情報処理装置、情報処理方法及びプログラム - Google Patents

情報処理装置、情報処理方法及びプログラム Download PDF

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JP7559943B2
JP7559943B2 JP2023523844A JP2023523844A JP7559943B2 JP 7559943 B2 JP7559943 B2 JP 7559943B2 JP 2023523844 A JP2023523844 A JP 2023523844A JP 2023523844 A JP2023523844 A JP 2023523844A JP 7559943 B2 JP7559943 B2 JP 7559943B2
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JPWO2022249373A5 (https=
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優太 畠山
穣 岡嶋
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NEC Corp
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    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound

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JP2023523844A 2021-05-27 2021-05-27 情報処理装置、情報処理方法及びプログラム Active JP7559943B2 (ja)

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JPWO2022249373A5 JPWO2022249373A5 (https=) 2024-02-19
JP7559943B2 true JP7559943B2 (ja) 2024-10-02

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Publication number Priority date Publication date Assignee Title
JP7694211B2 (ja) * 2021-07-06 2025-06-18 富士通株式会社 評価プログラム、評価方法および情報処理装置
US12443886B1 (en) * 2024-04-15 2025-10-14 NeuRealities, Inc. Techniques for adjusting mixed reality graphical environments using machine learning

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CZYZEWSKI, Maciej A.，"batchboost: REGULARIZATION FOR STABILIZING TRAINING WITH RESISTANCE TO UNDERFITTING & OVERFITTING"，arXiv.org [online]，arXiv:2001.07627v1，米国，Cornell University，2020年，pp.1-6，[検索日 2021.07.08], インターネット:<URL: https://arxiv.org/pdf/2001.07627v1>
KABRA, Anubha et al.，"MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance"，arXiv.org [online]，arXiv:2009.01571v1，米国，Cornell University，2020年，[検索日 2021.07.08], インターネット:<URL:https://arxiv.org/pdf/2009.01571v1>
渡邊 正人 ほか，「能動学習のための教師なしアノテーション対象データ選択手法」，情報処理学会研究報告 コンピュータビジョンとイメージメディア（ＣＶＩＭ） [online]，情報処理学会，2018年，第2018-CVIM-213巻, 第38号，pp.1-8，[検索日 2018.09.19], インターネット:<URL:https://ipsj.ixsq.nii.ac.jp/ej/?action=repository_uri&item_id=191383&file_id=1&file_no=1>

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JPWO2022249373A1 (https=) 2022-12-01
WO2022249373A1 (ja) 2022-12-01

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