A training algorithm for optimal margin classifiers

By Bernhard E. Boser, Isabelle M Guyon, Vladimir Naumovich Vapnik. 1992

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In Statistical Learning Theory

Citations in the corpus

Scaling and Benchmarking Self-Supervised Visual Representation Learning (2019, methodology): “We train linear SVMs [7] (with 3-fold cross validation to select the cost parameter) on fixed feature representations obSymbol Description”
Reconciling modern machine-learning practice and the classical bias–variance trade-off (2018, background): “The RFF model family H N with N (complex-valued) parameters consists of functions h : R d → C of the form [8]), the random classes H N are computationally attractive to use when the sample size n is large but the number of parameters N is small compared to n .”
MLS: A Large-Scale Multilingual Dataset for Speech Research (2020, methodology): “The classiﬁer is a SVM [16] with RBF kernel trained on 1172 speakers from train-clean-100 and train-clean-360 subsets of LibriSpeech .”
Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “The positive definiteness of the kernel function guarantees the existence of a dot product space H and a mapping φ : X → H such that k(x,y) = 〈φ(x), φ(y)〉H (Aronszajn 1950) without needing to compute φ explicitly (Boser et al. 1992, Cortes and Vapnik 1995, Vapnik 2000, Schölkopf and Smola 2002).”
Stealing Machine Learning Models via Prediction APIs (2016, methodology): “Kernel methods are commonly used to efficiently extend support vector machines (SVM) to nonlinear classifiers [14], but similar techniques can be applied to logistic regression [57].”
Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, neutral): “in [17].”
Security analysis of online centroid anomaly detection (2010, background): “In the extreme case, an algorithm learns online by updating its hypothesis after every data point it has received.”
Deep Weighted Averaging Classifiers (2018, background): “There is, of course, a long history in machine learning of making predictions directly in terms of training instances, including nearest neighbor methods [9], kernel methods, including support vector machines [5, 8], and transductive learners more broadly [52].”
Computing functions of random variables via reproducing kernel Hilbert space representations (2015, neutral): “…in machine learning, such as Support Vector Machines or Kernel PCA, are based on mapping the data into a reproducing kernel Hilbert space (RKHS) H (Boser et al. 1992; Schölkopf and Smola 2002; Shawe-Taylor and Cris-tianini 2004; Hofmann et al. 2008; Steinwart and Christmann 2008), where the…”
Fastfood: Approximate Kernel Expansions in Loglinear Time (2014, methodology): “…have proven to be a highly successful technique for solving many problems in machine learning, ranging from classification and regression to sequence annotation and feature extraction (Boser et al., 1992; Cortes and Vapnik, 1995; Vapnik et al., 1997; Taskar et al., 2004; Schölkopf et al., 1998).”
Robustness and Regularization of Support Vector Machines (2008, background): “Support Vector Machines (SVMs for short) originated in Boser et al. (1992) and can be traced back to as early as Vapnik and Lerner (1963) and Vapnik and Chervonenkis (1974).”
Introduction to Online Convex Optimization (2016, background): “Support vector machines were introduced in (31; 22), see also the book of Schölkopf and Smola (95).”
Deep Learning using Linear Support Vector Machines (2013, methodology): “Support vector machine is an widely used alternative to softmax for classiﬁcation (Boser et al., 1992).”
Kernel methods in machine learning (2007, neutral): “[23] were the first to use kernels to construct a nonlinear estimation algorithm, the hard margin predecessor of the Support Vector Machine, from its linear counterpart, the generalized portrait (Vapnik [139] and Vapnik and Lerner [145]).”
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020, background): “batch of classes and a battery of Support Vector Machines (SVMs) [7] to incrementally learn new classes.”
A Comprehensive Survey of Deep Learning for Image Captioning (2018, positive): “They are then passed to a classifier such as Support Vector Machines (SVM) [17] in order to classify an object.”