Dual Fuzzy-Possibilistic Co-clustering for Document Categorization

By Jun Yang, Rong Yan, Alexander G. Hauptmann. 2007

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Citations in the corpus

Few-Shot Adversarial Domain Adaptation (2017, methodology): “Finally, some use regularization to improve the fit on the target domain [4, 1, 62, 10, 3, 8].”
Simultaneous Deep Transfer Across Domains and Tasks (2015, background): “For the supervised adaptation scenario, when a limited amount of labeled data is available in the target domain, some approaches have been proposed to learn a target classifier regularized against the source classifier [32, 2, 1].”
Deep Domain Confusion: Maximizing for Domain Invariance (2014, background): “In the last few years several methods for visual domain adaptation have been suggested to overcome this issue [10, 33, 2, 29, 25, 22, 17, 16, 19, 20], but were limited to shallow models.”
LSDA: Large Scale Detection through Adaptation (2014, background): “, feature space transformations [12], model adaptation approaches [13, 14] and joint feature and model adaptation [15, 16].”
Meta-Transfer Learning for Few-Shot Learning (2018, background): “Transfer learning What and how to transfer are key issues to be addressed in transfer learning, as different methods are applied to different source-target domains and bridge different transfer knowledge [34, 56, 54, 58].”
Taskonomy: Disentangling Task Transfer Learning (2018, background): “Domain adaption seeks to render a function that is developed on a certain domain applicable to another [44, 99, 5, 80, 52, 26, 36].”
Efficient Learning of Domain-invariant Image Representations (2013, methodology): “We implement the transform learning and then apply both a KNN classifier (as originally proposed) and an SVM classifier.”
One-Shot Adaptation of Supervised Deep Convolutional Models (2013, methodology): “Many visual domain adaptation methods have been put forth to compensate for dataset bias [7, 22, 1, 20, 18, 16, 13, 12, 14, 15], but are limited to shallow models.”
Unified Deep Supervised Domain Adaptation and Generalization (2017, background): “The third one regularizes a classifier trained on a source distribution to work well on a target distribution [4, 1, 66, 15, 3, 12].”
Meta-Transfer Learning Through Hard Tasks (2019, methodology): “Different methods are applied to different source-target domains and bridge different transfer knowledge [9], [47]–[50].”
Explicit Inductive Bias for Transfer Learning with Convolutional Networks (2018, neutral): “For example, Yang et al. (2007) proposed an adaptive SVM (A-SVM), which regu-larizes the squared difference between the parameter vector and an initial parameter vector that is learned from the source database.”
Discovering Attribute Shades of Meaning with the Crowd (2015, methodology): “We use the resulting image-label pairs to train a discriminative classifier, using the adaptive support vector machine (SVM) objective of Yang et al (2007) to regularize its parameters to be similar to those of the consensus model.”
Continual Learning in Linear Classification on Separable Data (2023, methodology): “, extending our results to non-separable data (perhaps in the spirit of Yang et al. (2007)), multiclass classification with cross-entropy loss (similarly to Appendix 4.”
Learning visual biases from human imagination (2014, methodology): “A common transfer learning method for SVMs is to change the regularization term ||w||(2)2 to ||w − c||(2)2 where c is the prior [29, 37].”
Learning to Learn: Model Regression Networks for Easy Small Sample Learning (2016, methodology): “In addition, adaptive SVM and its variants present SVM-based model adaptation by combining classifiers learned on related categories [47, 102, 23, 2, 3, 53, 88, 97].”
Tabula rasa: Model transfer for object category detection (2011, methodology): “The idea, originally introduced by [14, 27], is to learn from the source model w by regularizing the distance between the learned model w and w.”
Learning Categories From Few Examples With Multi Model Knowledge Transfer (2014, methodology): “Yang et al. [25] presented a method to transfer the source information originally coded into an SVM model.”