Results 241 to 250 of about 219,224 (269)
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2018
Supervised machine learning techniques developed in the Probably Approximately Correct, Maximum A Posteriori, and Structural Risk Minimiziation frameworks typically make the assumption that the test data a learner is applied to is drawn from the same distribution as the training data.
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Supervised machine learning techniques developed in the Probably Approximately Correct, Maximum A Posteriori, and Structural Risk Minimiziation frameworks typically make the assumption that the test data a learner is applied to is drawn from the same distribution as the training data.
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Approximation Methods for Supervised Learning
Foundations of Computational Mathematics, 2005Let ź be an unknown Borel measure defined on the space Z := X × Y with X ź źd and Y = [-M,M]. Given a set z of m samples zi =(xi,yi) drawn according to ź, the problem of estimating a regression function fź using these samples is considered. The main focus is to understand what is the rate of approximation, measured either in expectation or probability,
Kerkyacharian, G. +3 more
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2006
In recent years, there has been considerable interest in non-standard learning problems, namely in the so-called semi-supervised learning scenarios. Most formulations of semisupervised learning see the problem from one of two (dual) perspectives: supervised learning (namely, classification) with missing labels; unsupervised learning (namely, clustering)
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In recent years, there has been considerable interest in non-standard learning problems, namely in the so-called semi-supervised learning scenarios. Most formulations of semisupervised learning see the problem from one of two (dual) perspectives: supervised learning (namely, classification) with missing labels; unsupervised learning (namely, clustering)
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Mismatched Supervised Learning
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022Xun Xian +2 more
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Self-supervised Learning: A Succinct Review
Archives of Computational Methods in Engineering, 2023Munish Kumar +2 more
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Self-supervised Learning: Generative or Contrastive
IEEE Transactions on Knowledge and Data Engineering, 2021Fanjin Zhang, Xiao Liu
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Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Longlong Jing, Yingli Tian
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A Survey on Deep Semi-Supervised Learning
IEEE Transactions on Knowledge and Data Engineering, 2023Xiangli Yang, Zixing Song, Irwin KING
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