Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)

By Vladimir Naumovich Vapnik. 1982

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Counterfactual reasoning and learning systems: the example of computational advertising (2012, background): “Better models are sometimes obtained by deliberately avoiding to reproduce the true mechanisms (Vapnik, 1982, section 8.6).”
A Model of Inductive Bias Learning (2000, methodology): “For Boolean learning problems (pattern classification) these parameters are the bias learning analogue of theVapnik-Chervonenkis dimension(Vapnik, 1982; Blumer et al., 1989).”
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning (2020, methodology): “This question has been elemental in later formalization of frameworks for empirical risk optimization (Tsypkin, 1971; Vapnik, 1982).”
Testing the Manifold Hypothesis (2013, background): “This is the viewpoint of modern Statistical Learning Theory [54].”
Learning using privileged information: similarity control and knowledge transfer (2015, background): “strongly uniformly converges to the function f(x, α0) that minimizes the error rate on the closure of ∪k=1Sk (Vapnik and Chervonenkis, 1974), (Vapnik, 1982), (Devroye et al.”
Online Learning and Stochastic Approximations (1998, background): “This contribution borrows the framework introduced by the Russian school (Tsypkin, 1971; Vapnik, 1982).”
Spectral Algorithms for Supervised Learning (2008, background): “…amount of literature has pointed out the connection between algorithms in learning theory and regularization methods in inverse problems (see, e.g., Vapnik, 1982, 1998; Poggio & Girosi, 1992; Evgeniou, Pontil, & Poggio, 2000; Hastie, Tibshirani, & Friedman, 2001; Sch¨olkopf & Smola, 2002; De Vito,…”
SemiBoost: Boosting for Semi-Supervised Learning (2009, methodology): “…proposed semi-supervised learning approach are: 1) performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient computation by the iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models.”
Support-Vector Networks (1995, methodology): “The conceptual part of this problem was solved in 1965 (Vapnik, 1982) for the case of optimal hyperplanes for separable classes.”
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data (2019, methodology): “Support vector machines SVM was ﬁ rst described in Cortes and Vapnik (1995) based on the work of Vapnik (1982) and is a supervised learning technique commonly used in a range of remote sensing applications.”
The Tradeoffs of Large Scale Learning (2007, background): “Large-scale learning problems are subject to a qualitatively different tradeoff involving the computational complexity of the underlying optimization algorithms in non-trivial ways.”
Sharp adaptation for inverse problems with random noise (2002, methodology): “…type regularization techniques, recursive estimation procedures in Hilbert space, projection (or Galerkin) methods see Wahba (1977, 1990), Vapnik (1982), O'Sullivan (1986), Vainikko and Veretennikov (1986), Johnstone and Silverman (1990), Korostelev and Tsybakov (1993), Donoho (1995),…”