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Weakly privileged learning with knowledge extraction
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AI
DOI：10.1016/j.patcog.2024.110517.png)
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摘要
En 中文
Learning using privileged information (LUPI) has shown promise in improving supervised learning by embedding additional knowledge. However, its reliance on the assumption of readily available privileged information may not hold true in practical scenarios due to limitations in access or confidentiality. To address these challenges, this paper presents a novel weakly privileged learning (WPL) framework, integrating knowledge extraction methods within the LUPI context. An effective strategy is proposed to implement the WPL framework, where knowledge extraction techniques generate a weight matrix as weak privileged information. Extensive experiments employing various existing knowledge extraction techniques demonstrate that the proposed WPL outperforms traditional supervised learning and approaches the performance of standard privileged learning where privileged information is given in advance. This research establishes WPL as a promising learning paradigm, addressing limitations in privileged information availability and advancing the field of machine learning in practical settings.
Keyword:
Learning using privileged information (LUPI)
Weakly privileged learning
Knowledge extraction
Privileged information
Support vector machine
Classification
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
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RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Progressive privilege d knowle dge distillation for online action detectionPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Learning using privileged information: SVM plus and weighted SVM使用特权信息学习: SVM plus和加权SVMNEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏分享收藏Deep and interpretable regression models for ordinal outcomes有序结果的深度和可解释的回归模型PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏
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