The Constrained Weight Space SVM: Learning with Ranked Features

By Kevin Small, Byron C. Wallace, Carla E. Brodley, Thomas A. Trikalinos. 2011

45 citations recorded by the source corpus.

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In Statistical Learning Theory

Citations in the corpus

When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data (2021, methodology): “, 2017; 2018), or through placing priors over how features are weighted or extracted (Zaidan et al., 2007; Small et al., 2011; Zhang et al., 2016; Ross et al., 2017; Bao et al., 2018; Selvaraju et al., 2019; Liang et al., 2020; Stammer et al., 2020).”
Making deep neural networks right for the right scientific reasons by interacting with their explanations (2020, methodology): “Most importantly, this data augmentation procedure is model-agnostic, although alternatives indeed exist: (manually) adding a discovered data artifact to samples of other classes [50], contrastive examples [51], feature ranking [52] for SVMs and constraints on the input gradients for differentiable ”
ERASER: A Benchmark to Evaluate Rationalized NLP Models (2019, background): “Earlier efforts proposed extending standard discriminative models like Support Vector Machines (SVMs) with regularization terms that penalized parameter estimates which disagreed with provided rationales (Zaidan et al., 2007; Small et al., 2011).”
Rationale-Augmented Convolutional Neural Networks for Text Classification (2016, methodology): “This work has largely involved inserting constraints into the learning process that favor parameter values that align with a priori feature-label afﬁnities or rankings (Druck et al., 2008; Mann and McCallum, 2010; Small et al., 2011; Settles, 2011).”
Active Learning (2012, background): “, 2009), or constraints on the objective function (Druck et al., 2008; Liang et al., 2009; Small et al., 2011).”
Perceptual Annotation: Measuring Human Vision to Improve Computer Vision (2014, background): “, eye movements used to define discriminative image regions for feature extraction [14], [15], structured domain knowledge from human experts [16], [17], [18], and models of the typical human annotation process itself [19].”
Explanatory Interactive Machine Learning (2019, methodology): “Most importantly, this data augmentation procedure is model-agnostic, although alternatives indeed exist: Contrastive examples [ 40], feature ranking [35] for SVMs and constraints on the input gradients for differentiable models [27].”
Learning to Faithfully Rationalize by Construction (2020, background): “Prior work has exploited such signal during training (Zhang et al., 2016; Strout et al., 2019; Small et al., 2011).”
Group-Based Active Learning of Classification Models (2017, background): “Active learning has gained popularity in many fields such as computer vision (Salmani and Sridharan 2014; Settles, Craven, and Ray 2008), natural language processing (Druck, Settles, and McCallum 2009; Small et al. 2011) and bio-medical data mining (Hoi et al.”
Deploying an interactive machine learning system in an evidence-based practice center: abstrackr (2012, methodology): “These include active learning strategies [12, 8] and general classifier induction algorithms [6, 9].”
Using text mining for study identification in systematic reviews: a systematic review of current approaches (2015, methodology): “This study was focused on a text mining approach and utilised data from systematic reviews as its test scenario [71].”