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Co-training is a machine learning algorithm used when there are only small amounts of labeled data and large amounts of unlabeled data. One of its uses is in text mining for search engines. It was introduced by Avrim Blum and Tom Mitchell in 1998.
- Training
Training is teaching, or developing in oneself or others,...
- Training
Co-training is a semi-supervised learning technique in the domain of machine learning. It leverages both labeled and unlabeled data to improve the performance of classifiers. The technique was first introduced by Avrim Blum and Tom Mitchell in their 1998 paper, Combining Labeled and Unlabeled Data with Co-Training .
1 de nov. de 2015 · In this paper, we propose a general framework for co-training according to the diverse learners constructed in co-training. Specifically, we provide three types of co-training implementations, including co-training on multiple views, co-training on multiple classifiers, and co-training on multiple manifolds.
- Weifeng Liu, Yang Li, Dapeng Tao, Yanjiang Wang
- 2015
20 de mar. de 2021 · Summary. Co-training algorithm is one of the main methods of semi-supervised learning in machine learning, which explores the effective information in unlabeled data by multi-learner collaboration. Based on the development of co-training algorithm, the research work in recent years was further summarized in this article.
- Xin Ning, Xinran Wang, Shaohui Xu, Weiwei Cai, Liping Zhang, Lina Yu, Wenfa Li
- 2021
Co-training is a machine learning algorithm used when there are only small amounts of labeled data and large amounts of unlabeled data. One of its uses is in text mining for search engines. It was introduced by Avrim Blum and Tom Mitchell in 1998.
Co-training, a paradigm of semi-supervised learning, may alleviate effectively the data scarcity problem (i.e., the lack of labeled examples) in supervised learning. The standard two-view co-training requires the dataset be described by two views of attributes, and...
