Logistic regression with an auxiliary data source

By Xuejun Liao, Ya Xue, Lawrence Carin. 2005

226 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering

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Citations in the corpus

Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, positive): “ctive learning. Instance selection based DA methods exploit ideas from active learning [250] to select instances with best potentials to help the training process. Thus, the Migratory-Logit algorithm [251] explore, both the target and source data to actively select unlabeled target samples to be add”
Differentially Private Hypothesis Transfer Learning (2018, background): “In the transfer learning literature, there are four major approaches – instance re-weighting [22,17,11], feature representation transfer [32], hypothesis transfer [19,38,20] and relational knowledge transfer [26].”
A Survey on Transfer Learning (2010, background): “The first context can be referred to as instance-based transfer learning (or instance transfer) approach [6], [28], [29], [30], [31], [24], [32], [33], [34], [35], which assumes that certain parts of the data in the source domain can be reused for learning in the target domain by reweighting.”
Flexible Transfer Learning under Support and Model Shift (2014, background): “[14] relaxed the requirement that the training and testing examples be drawn from the same source distribution in the context of logistic regression.”
Topic-bridged PLSA for cross-domain text classification (2008, unknown): “active learning [14], and boosting [5].”
Cross-domain video concept detection using adaptive svms (2007, background): “[8], Wu and Dietterich [17], and Wu and Srihari [18] incorporate the (weighted) auxiliary data into the current training data to train a more reliable SVM or logistic regression classifier.”
Feature-based transfer learning with real-world applications (2010, background): “[107] proposed a new active learning method to select the unlabeled data in a target domain to be labeled with the help of the source domain data.”
Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification (2009, background): “Transfer learning can be categorized as (1) inductive transfer: there are a few labeled data in the target task, while there are a large amount of labeled [18] [19] [20] or unlabeled [21] data in the source task, (2) transductive transfer: there are no labeled data in the target task, while there ar”
A Decade Survey of Transfer Learning (2010–2020) (2020, background): “Therefore, some algorithms [77], [78] intend to remove all the different distribution data instead of assigning small weights to them.”
Smart Multitask Bregman Clustering and Multitask Kernel Clustering (2015, background): “In inductive transfer learning, there are some labeled data in the target domain, and there are a lot of labeled data [Dai et al. 2007b; Liao et al. 2005; Wu and Dietterich 2004] or no labeled data [Raina et al.”