Transferring Naive Bayes Classifiers for Text Classification

By Wenyuan Dai, Gui-Rong Xue, Qiang Yang, Yong Yu. 2007

415 citations recorded by the source corpus.

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Citations in the corpus

Transfer learning for cross-company software defect prediction (2012, methodology): “[17] proposed a transfer learning method based on EM algorithm, which can found a locally optimal a posteriori hypothesis under the target distribution.”
Generalized Transfer Subspace Learning Through Low-Rank Constraint (2014, background): “Transductive transfer learning (Arnold et al. 2007; Dai et al. 2007a,b), and specifically, domain adaptation (Daumé III and Marcu 2006; Blitzer et al. 2006, 2011; Daumé 2007; Sun et al. 2011; Glorot et al. 2011; Chen et al. 2011; Lopez-Paz et al. 2012; Zhang et al. 2012) transforms the source…”
A Literature Survey on Domain Adaptation of Statistical Classifiers (2007, methodology): “Dai et al. [2007] proposed an EM-based algorithm for domain adaptation, which can be shown to be equivalent to a semi-supervised EM algorithm [Nigam et al., 2000] except that Dai et al. proposed to estimate the trade-off parameter between labeled and unlabeled data using the KL-divergence between th”
Transfer Learning across Networks for Collective Classification (2013, background): “The first category is base d o instance transfer [4], [10], in which certain parts of the instances in the source domain can be reused for learning in the target domain via instance weighting.”
Feature-based transfer learning with real-world applications (2010, methodology): “In this experiment, we follow the preprocessing strategy in [47, 48] to create six data sets from this collection.”
Attention Models in Graphs (2018, background): “…can be useful in another dataset much like how transfer learning is used to train a model on one text corpora for application on another text dataset [Dai et al. 2007] or to train a model to recognize general shapes in a large image dataset to be applied to a sparser dataset [Oquab et al. 2014].”
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach (2011, background): “Finally, domain adaptation can be simply treated as a standard semi-supervised problem by ignoring the domain difference and considering the source instances as labeled data and the target ones as unlabeled data (Dai et al., 2007).”
Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews (2011, methodology): “…Bayes classifier (Dai et al. 2007a), Topic-bridged PLSA (Xue D ow nloaded from http://direct.m it.edu/coli/article-pdf/37/3/587/1812661/coli_a_00061.pdf by guest on 01 July 2021 et al. 2008), Co-Clustering–based classification (Dai et al. 2007b), and the two-stage approach (Jiang and Zhai 2007).”
Domain adaptation for object recognition: An unsupervised approach (2011, methodology): “This was followed by methods that combine co-training and domain adaptation using labels from either domains [36], and semi-supervised variants of the EM algorithm [14], label propagation[42] and SVM [18].”
DLID: Deep Learning for Domain Adaptation by Interpolating between Domains (2013, background): “Another set of approaches in this fixed representation view of the problem treats domain adaptation as a conventional semi-supervised learning (Bergamo & Torresani, 2010; Dai et al., 2007; Yang et al., 2007; Duan et al., 2012).”
A Survey on Text Classification: From Traditional to Deep Learning (2020, methodology): “[28] propose a transfer learning method named Naive Bayes Transfer Classification (NBTC) to settle the different distribution between the training set and the target set.”
Transfer learning using computational intelligence: A survey (2015, methodology): “To adapt the naïve Bayes classifier from the training data to the test data, [17] proposed a novel naïve Bayes transfer learning (NBTL) classification algorithm for text categorization.”