Support Vector Method for Function Approximation, Regression Estimation and Signal Processing

By Vladimir Naumovich Vapnik, S. Golowich, Alex Smola. 1996

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “vector regression (SVG) [104] and extremely randomized trees (ERT) [41], were applied to fit the approximated Q-functions to the generated trial data.”
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; Sch¨olkopf et al., 1998).”
Kernel methods in machine learning (2007, neutral): “[144] using the so-called ǫ-insensitive loss function.”
Reinforcement Learning Strategies for Clinical Trials in Non-small Cell Lung Cancer (2011, methodology): “In order to successfully handle the complex fact of heterogeneity in treatment across individuals as well as right-censored survival data, we modify the support vector regression (SVR) approach (Vapnik et al., 1997) within a Q-learning framework to fit potentially nonlinear Q-functions for each of t”
Analyzing feature generation for value-function approximation (2007, methodology): “In the area of value-function approximation for reinforcement learning or the approximate solution of Markov decision processes (MDPs), which have reaped few of the benefits of techniques such as boosting (Freund & Schapire, 1995) or the kernel trick (Vapnik et al., 1997), the challenge of feature s”
Support vector machine applications in the field of hydrology: A review (2014, methodology): “SVMs have been proven to be n extremely robust and efficient algorithm for classification [5] nd regression [6,7].”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, methodology): “SVM is a statistical learning algorithm (Vapnik et al. 1997) which is an important method to estimate forest biophysical parameters using remote sensing data (Mountrakis et al. 2011; Marabel and Alvarez-Taboada 2013).”
A Review on Video-Based Human Activity Recognition (2013, methodology): “On the other hand, discriminative models learn the conditional (posterior) probability distribution P(Y|X)over an unobserved fixed-length class label Y on a given observed fixed-length feature vector X, such as support vector machines (SVMs) [79,80], relevance vector machines (RVMs) [81,82] and arti”
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces (2004, methodology): “…of reproducing kernel Hilbert spaces is related to their role in algorithms such as the support vector machine and kernel PCA (Boser et al., 1992, Vapnik et al., 1997, Schölkopf et al., 1998), where the kernel function allows linear operations in function spaces to be performed in a…”
Support Vector Regression (2007, methodology): “A version of a SVM for regression has been proposed in 1997 by Vapnik, Steven Golowich, and Alex Smola [6].”
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning (2002, methodology): “Mostly, these methods target problems in classification and diagnostics, although several extensions to regression problems exist (e.g., Vapnik, Golowich, and Smola, 1996).”