Semi Supervised Logistic Regression
By Massih-Reza Amini, Patrick Gallinari. 2002
104 citations recorded by the source corpus.
Subfields: Statistics and Probability, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- B. V. Vijaya Kumar
- Wangmeng Zuo
- Yoshua Bengio
- Yves Grandvalet
- Yang Zou
- Amir Najafi
- Masanori Koyama
- Takeru Miyato
In Representation & Self-Supervised Learning
- Takeru Miyato
- Masanori Koyama
- Shin-ichi Maeda
- Yoshua Bengio
- Quoc V. Le
- Zhiding Yu
- Amir Najafi
- Weishi Zheng
Citations in the corpus
- MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection (2021, background): “Self-training has been widely investigated in semi-supervised learning [1, 10, 6, 27, 22, 35].”
- Confidence Regularized Self-Training (2019, methodology): “Related works Self-training: Self-training has been widely investigated in semi-supervised learning [65, 1, 18].”
- Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation (2017, methodology): “In this section, we propose a weighted domain adaptation network (WDAN) model, which is in essential an extension of the semi-supervised logistic regression [1] by adding the WMMD term and incorporating with CNN.”
- Self-Training: A Survey (2022, unknown): “The main difference between generative and discriminant techniques is that discriminant approaches find directly the prediction function without making any assumption on how the data are generated [Amini and Gallinari, 2002; Oliver et al., 2018].”
- Robustness to Adversarial Perturbations in Learning from Incomplete Data (2019, methodology): “We take a known family of SSL approaches, called self-learning [13, 35], and then combine it with DRL.”
- Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks (2014, unknown): “This work is also loosely related to the use of unlabeled data for regularizing supervised algorithms, for example self-training [19], entropy regularization [20] or Discriminative and Shareable Feature Learning [21].”
- Semi-supervised Learning by Entropy Minimization (2004, background): “Finally, we also illustrate that them thod can also be far superior to manifold learning in high dimension spaces.”