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Semi-supervised learning [PDF]
Machine Learning, 1989 The distribution-independent model of (supervised) concept learning due to Valiant (1984) is extended to that of semi-supervised learning (ss-learning), in which a collection of disjoint concepts is to be simultaneously learned with only partial information concerning concept membership available to the learning algorithm.
Raymond A. Board, Leonard Pitt
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Learning to Learn in a Semi-supervised Fashion [PDF]
, 2020 To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme. We particularly consider that labeled and unlabeled data share disjoint ground truth label sets, which can be seen tasks like in person re-identification or image retrieval.
Yun-Chun Chen +2 more
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Human Semi‐Supervised Learning [PDF]
Topics in Cognitive Science, 2013 AbstractMost empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real‐world learning scenarios, however, are semi‐supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a ...
Bryan R. Gibson +2 more
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Contrastive Semi-Supervised Learning for ASR [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Pseudo-labeling is the most adopted method for pre-training automatic speech recognition (ASR) models. However, its performance suffers from the supervised teacher model's degrading quality in low-resource setups and under domain transfer. Inspired by the successes of contrastive representation learning for computer vision and speech applications, and ...
Alex Xiao +2 more
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Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry
Sensors, 2021 Nowadays, soft sensor techniques have become promising solutions for enabling real-time estimation of difficult-to-measure quality variables in industrial processes.
Youwei Li +4 more
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Information, 2021 Smart grids integrate advanced information and communication technologies (ICTs) into traditional power grids for more efficient and resilient power delivery and management, but also introduce new security vulnerabilities that can be exploited by ...
Ruobin Qi +3 more
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Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [PDF]
Jisuanji kexue, 2022 Consistency-based semi-supervised learning methods typically use simple data augmentation methods to achieve consistent predictions for both original inputs and perturbed inputs.The effectiveness of this approach is difficult to be guaranteed when the ...
XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo
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LMGAN: Linguistically Informed Semi-Supervised GAN with Multiple Generators
Sensors, 2022 Semi-supervised learning is one of the active research topics these days. There is a trial that solves semi-supervised text classification with a generative adversarial network (GAN).
Whanhee Cho, Yongsuk Choi
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Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 We propose a new weakly supervised approach for classification and clustering based on mixture models. Our approach integrates multi-level pairwise group and class constraints between samples to learn the underlying group structure of the data and ...
Adama Nouboukpo, Mohand Saïd Allili
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Semi-supervised Learning for Anomalous Trajectory Detection [PDF]
, 2008 A novel learning framework is proposed for anomalous behaviour detection in a video surveillance scenario, so that a classifier which distinguishes between normal and anomalous behaviour patterns can be incrementally trained with the assistance of a ...
Fisher, Bob +3 more
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