Semi-Supervised Regression with Co-Training

By Zhi-Hua Zhou, Ming Li. 2005

316 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Dacheng Tao
2. Chang Xu
3. Yi Yang
4. Xiang Bai
5. Liang Zheng
6. Alan L. Yuille
7. Han-Jia Ye
8. Yoshua Bengio

Citations in the corpus

• A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, methodology): “Co-training regressors [99] uses two k-nearest neighbor regressors with different distance metrics.”
• A Survey on Multi-view Learning (2013, methodology): “Zhou and Li (2005b) proposed another co-training style semi-supervised algorithm called tri-training, which does not require that the instance space be described with sufficient and redundant views, nor does it put any constraints on the supervised algorithm, as do Goldman and Zhou (2000).”
• ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment (2014, methodology): “For STS task, we utilized CoReg algorithm (Zhou and Li, 2005) which uses two k NN regressors to perform co-training paradigm.”
• Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis (2019, background): “unsupervised learning [42], semi-supervised learning [43], [44], weakly-supervised learning [21] and multi-agent learning [45].”
• Semi-Supervised and Unsupervised Deep Visual Learning: A Survey (2022, background): “Semi-supervised learning [7], [8] and unsupervised learning [9], [10], [11], [12] stand out as two most representative paradigms for leveraging unlabeled data.”
• Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation (2020, methodology): “Semi-supervised learning aims at learning models with limited labeled data and a large proportion of unlabeled data [3,5,70,71].”
• Analyzing Co-training Style Algorithms (2007, methodology): “…used in the standard co-training algorithm [5], the two different supervised learning algorithms used in Goldman and Zhou’s algorithm [8], and the different configurations of the base learner used in Zhou and Li’s algorithm [16] are actually exploited to make the classifiers to have large difference.”
• Efficient co-regularised least squares regression (2006, methodology): “Zhou and Li (2005) apply co-training to kNN regression.”
• Introduction to Semi-Supervised Learning (2009, unknown): “• semi-supervised regression [25, 47, 159, 205];”
• Semi-supervised learning by disagreement (2010, unknown): “Therefore, Zhou and Li [87] proposed a predictive confidence estimation criterion for disagreement-based semi-supervised learning method.”
• Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples (2007, background): “Recently, a co-training-style algorithm for semisupervised regression has been proposed [44], which does not require sufficient and redundant attribute subsets.”
• GIFT: A Real-Time and Scalable 3D Shape Search Engine (2016, background): “( This formula is inspired by “co-training” [45].”
• A Survey of Optimization Methods From a Machine Learning Perspective (2019, unknown): “including classification tasks [70], [71]; regression tasks [72]; clustering tasks [73], [74]; and dimensionality reduction tasks [75], [76].”
• Methodologies for Cross-Domain Data Fusion: An Overview (2015, unknown): “Zhou and Li [92] expand the co-training”
• A survey on semi-supervised learning (2019, methodology): “Zhou and Li (2005b) proposed tri-training, where three classifiers are alternately trained.”