Conﬁdence-Weighted Linear Classiﬁcation

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Citations in the corpus

Adaptive Bound Optimization for Online Convex Optimization (2010, methodology): “Perhaps the closest algorithms in spirit to our diagonal adaptation algorithm are conﬁdence-weighted linear classiﬁcation [Drezde et al., 2008] and AROW [Crammer et al., 2009], in that they make different-sized adjustments for different coordinates.”
Learning using privileged information: SV M+ and weighted SVM (2013, background): “…to papers on cost-sensitive learning (Margineantu, 2002; Zadrozny et al., 2003), sample bias correction (Heckman, 1979; Cortes et al., 2010), domain adaptation (Shimodaira, 2000; Sugiyama & Müller, 2005), online learning (Dredze et al., 2008), and active learning (Beygelzimer et al., 2009).”
Online Deep Learning: Learning Deep Neural Networks on the Fly (2017, unknown): “Many techniques are based on maximum-margin classiﬁcation, from Perceptron (Rosenblatt 1958) to Online Gradient De-scent (Zinkevich 2003), Passive Aggressive (PA) algorithms (Crammer et al. 2006), Conﬁdence-Weighted (CW) Algorithms, (Dredze, Crammer, and Pereira 2008) etc.”
Multi-domain learning by confidence-weighted parameter combination (2010, unknown): “Confidence-weighted (CW) linear classification (Dredze et al. 2008; Crammer et al. 2008) is an online learning algorithm for linear classifiers.”
Learning with Noisy Labels (2013, methodology): “3A family of methods proposed by Crammer and coworkers [Crammer et al., 2006, 2009, Dredze et al., 2008] could be compared to, but [Crammer and Lee, 2010] show that the 2 NHERD variants perform the best.”
Online multimodal deep similarity learning with application to image retrieval (2013, unknown): “In literature, a variety of algorithm [6, 37, 9, 11, 52, 46] have been proposed for online learning.”
Multi-domain spoken language understanding with transfer learning (2009, unknown): “To this end, we implemented four online learning algorithms; averaged perceptron (AP) (Collins, 2002), approximate large margin algorithm (ALMA) (Daumé and Marcu, 2005), passive aggressive (PA) (Crammer et al., 2006), and confidence weighted classification (CW) (Dredze et al., 2008).”
Building Watson: An Overview of the DeepQA Project (2010, unknown): “(Dredze, Crammer, and Pereira 2008).”
Supervised Representation Learning: Transfer Learning with Deep Autoencoders (2015, unknown): “…level, which aims to learn weights for the source domain labeled data, such that the reweighted source domain instances look similar to the target domain data instances [Dai et al., 2007b; Gao et al., 2008; Xing et al., 2007; Jiang and Zhai, 2007; Zhuang et al., 2010; Crammer et al., 2012].”
Online Methods for Multi-Domain Learning and Adaptation (2008, methodology): “Conﬁdence-weighted (CW) linear classiﬁcation (Dredze et al., 2008), a new online algorithm, maintains a probabilistic measure of parameter conﬁ-dence, which may be useful in combining parameters from different domain distributions.”
Multi-Domain Learning: When Do Domains Matter? (2012, unknown): “ARTY ) for which a given MDL method using true domain information was signiﬁcantly better , signiﬁcantly worse , or not signiﬁcantly different ( equal ) as compared to using randomized domain information with the same MDL method. impacted MDL.”
Identifying suspicious URLs: an application of large-scale online learning (2009, background): “dence by modeling uncertainty in weight wi with a Gaussian distribution N (µi,Si)(Dredze et al., 2008; Crammer et al., 2009).”
Malicious URL Detection using Machine Learning: A Survey (2017, background): “…[84], [87], [106] Online Learning First-order algorithms [13], [28], [37], [73], [109]–[111] Second-order algorithms [28], [36], [39], [76], [85], [113], [114] Cost-Sensitive Online Learning [115], [116] Online Active learning [116], [121], [123] Representation Learning Feature Selection [35],…”
Collaborating between Local and Global Learning for Distributed Online Multiple Tasks (2015, unknown): “Conﬁdence-weighted learning maintains a Gaussian distribution over the linear prediction models, which is also used to guide the model updating process [17].”