Results 291 to 300 of about 341,780 (320)
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2005
For many classification problems, unlabeled training data are inexpensive and readily available, whereas labeling training data imposes costs. Semi-supervised classification algorithms aim at utilizing information contained in unlabeled data in addition to the (few) labeled data.
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For many classification problems, unlabeled training data are inexpensive and readily available, whereas labeling training data imposes costs. Semi-supervised classification algorithms aim at utilizing information contained in unlabeled data in addition to the (few) labeled data.
openaire +1 more source
Semi-supervised distribution learning
BiometrikaAbstract This study addresses the challenge of distribution estimation and inference in a semi-supervised setting. In contrast to prior research focusing on parameter inference, this work explores the complexities of semi-supervised distribution estimation, particularly the uniformity problem inherent in functional processes.
Mengtao Wen +4 more
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A Survey on Deep Semi-Supervised Learning
IEEE Transactions on Knowledge and Data Engineering, 2023Xiangli Yang, Zixing Song, Irwin King
exaly
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)
openaire +1 more source
A Survey on Semi-supervised Learning for Delayed Partially Labelled Data Streams
ACM Computing Surveys, 2023Heitor Murilo Gomes +2 more
exaly