Support Vector Machine Active Learning with Applications to Text Classification

By Simon Tong, Daphne Koller. 2000

3,523 citations recorded by the source corpus.

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Citations in the corpus

Learning from Dialogue after Deployment: Feed Yourself, Chatbot! (2019, background): “cussed in this work, as is active learning (Tong and Koller, 2001) and predictive modeling (Schmidhuber and Huber, 1991).”
Deep Active Learning with a Neural Architecture Search (2018, background): “The literature on active learning in the context of classical models such as SVMs is extensive [4, 5, 23, 2, 1, 13], and clearly beyond the scope of this paper.”
Making deep neural networks right for the right scientific reasons by interacting with their explanations (2020, background): “Prototypical active learners include max-margin [43] and Bayesian approaches [44]; recently, deep variants have been proposed [45].”
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees (2018, background): “Similar to [19], our active learner ` has three components: (q, f, I).”
Incremental Learning from Scratch for Task-Oriented Dialogue Systems (2019, background): “To find new concepts, the heuristic uncertainty estimation methods (Tong and Koller, 2001; Culotta and McCallum, 2005) in active learning (Balcan et al.”
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors (2018, background): “Approaches include maximum entropy (Joshi et al., 2009), distance from the decision boundary (Tong and Koller, 2001), pseudo labelling high conﬁdence examples (Wang et al., 2017), and mixtures of information density and uncertainty measures (Li and Guo, 2013).”
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning (2020, methodology): “In models such as SVMs for binary classification this can intuitively be explained based on the margins (Tong and Koller, 2001).”
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (2019, background): “, 2015) to topic classification (Tong and Koller, 2002), but high performance often depends on the size and quality of training data, which is often tedious to collect.”
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets (2020, methodology): “In this paper we consider the pool-based AL model, a commonly adapted approach in text classification problems (Hu et al., 2016; Krithara et al., 2006; Tong and Koller, 2001; Nigam and McCallum, 1998).”
Human computation: a survey and taxonomy of a growing field (2011, background): “Then, it analyzes them to identify which examples would have the most training benefit if annotations were made available [61].”
Adaptive computation and machine learning (1998, positive): “* groups from the Newsgroups data set and the goal is to classify the ibm category versus the rest (Tong and Koller, 2001).”
Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions (2012, methodology): “In this case, the typical active approach uses margins as the main criterion and selects input points that maximize them [34,35].”
Less is More: Active Learning with Support Vector Machines (2000, background): “See Tong and Koller (2000) in this volume for parallel research on this topic.”
Learning on the border: active learning in imbalanced data classification (2007, methodology): “5 Online SVMs for Active Learning The ACTIVE LASVM algorithm implements two dramatic speedups with respect to existing active learning algorithms such as (Campbell et al., 2000; Schohn and Cohn, 2000; Tong and Koller, 2000).”
Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey (2009, methodology): “Two selective AL sampling methods were considered: Simple Margin (Tong and Koller, 2000) and Error Reduction (Roy and McCallum, 2001).”
Active Learning Literature Survey (2009, background): “An important question is: does active learning actually work? Most empirical results in the literature suggest yes (e.g., Cohn et al., 1994; Thompson et al., 1999; Tong and Koller, 2000; Tur et al., 2005; Settles and Craven, 2008), however, there are caveats.”