The Nature of Statistical Learning

By Vladimir Naumovich Vapnik. 1995

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In Statistical Learning Theory

Citations in the corpus

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (2015, methodology): “, 1984], `1-regularized logistic regression, support vector machines [Vapnik, 1995], random forests [Breiman, 2001], and Bayesian CART [Dension et al.”
An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “SVM has been first introduced by Vapnik (1995).”
Estimating treatment effect heterogeneity in randomized program evaluation (2013, methodology): “In estimating (β, γ), we adapt the support vector machine (SVM) classifier and place separate LASSO constraints over each set of coefficients [Vapnik (1995), Tibshirani (1996), Bradley and Mangasarian (1998), Zhang (2006)].”
On the Schoenberg Transformations in Data Analysis: Theory and Illustrations (2010, methodology): “Higher-order “principled” embed-dings, pioneered by the work of Vapnik (1995) and embodied in this article by the class of Schoenberg transformations, are arguably about to be incorporated in standard Data Analysis, to be routinely used in applications, and taught at graduate and undergraduate…”
How to Explain Individual Classification Decisions (2009, methodology): “For other classiﬁcation methods such as Support Vector Machines which do not provide a probability function as its output in Section 3 we give an example for an estimation method starting from Deﬁnition 1.”
From machine learning to machine reasoning (2011, background): “The statistical nature of learning is now well understood (e.g., Vapnik, 1995).”
Empirical Bernstein Bounds and Sample-Variance Penalization (2009, methodology): “We will use the classical doublesample method ([10], [1]), but we have to pervert it somewhat to adapt it to the nonlinearity of the empirical standarddeviation functional.”
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle (2015, background): “In the General Learning Setting of Vapnik (1995), a learning problem is characterized by a triplet (Z,H, `).”
Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification (2016, methodology): “For the triplet comparison based network, we learn the CIR by ranking SVM (RankSVM) [30].”
Assigning Polarity Scores to Reviews Using Machine Learning Techniques (2005, background): “vector regression (SVR) (Vapnik 1995) to determine the SP scores of reviews.”
Discriminative Direction for Kernel Classifiers (2001, methodology): “Given a training set of l pairs {(xk, yk)}k=1, where xk ∈ R are observations and yk ∈ {−1, 1} are corresponding labels, and a kernel function K : R × R 7→ R, (with its implied mapping function ΦK : R 7→ F), the Support Vector Machines (SVMs) algorithm [8] constructs a classifier by implicitly mappin”
Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space (2002, background): “The common aim of support vector machines, regularization networks, Gaussian processes and spline methods (Vapnik, 1998, Girosi, 1998, Williams, 1998, Wahba, 1990, Cristianini and Shawe-Taylor, 2000) is to address the poor generalization properties of existing nonlinear regression techniques.”
Random-Walk Term Weighting for Improved Text Classification (2007, unknown): “SVM [27] is a state-of-the-art machine learning approach based on decision plans.”
Behavioral detection of malware on mobile handsets (2008, methodology): “Recently, Support Vector Machines (SVMs) [40]—a supervised learning algorithm based on the pioneering work of Vapnik [48] and Joachims [26] on statistical learning theory—have been successfully applied in a number of”
QUADRATIC DEPENDENCE MEASURE FOR NONLINEAR BLIND SOURCES SEPARATION (2003, methodology): “One may note that the kernel I y is related to Mercer kernels which are used especially in Support Vector Machine [13] .”