Monocular 3 D tracking of deformable surfaces using sequential second order cone programming

By Shuhan Shena, Yuncai Liua, Wu-Sheng Lub. 1979

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Citations in the corpus

Variance-based Regularization with Convex Objectives (2016, background): “Our first corollary shows that Theorem 3 applies to standard Vapnik-Chervonenkis (VC) classes.”
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).”
Rejoinder: Local Rademacher complexities and oracle inequalities in risk minimization (2006, background): “…in my paper primarily because in most of the learning theory and statistical applications I had in mind it is hard to take real advantage of making the slices thin and, on the other hand, there is a need to take care of the assumption that the class is star-shaped (which is a minor difficulty).”
Improved website fingerprinting on Tor (2013, methodology): “The Support Vector Machine (SVM), proposed by Vapnik and Chervonenkis [16], is a classifier that was used by previous authors for website fingerprinting [2, 12] and was shown to be fairly successful.”
Explaining AdaBoost (2013, background): “In formalizing these conditions, Vapnik and Chervonenkis [34, 35] established a foundation for understanding the fundamental nature of learning, laying the groundwork for the design of effective and principled learning algorithms.”
Introduction to Statistical Learning Theory (2004, methodology): “Symmetrization lemmas can be found in Giné and Zinn [74] and Vapnik and Chervonenkis [18, 15].”
Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data Analysis (2001, methodology): “By searching for large margin hyperplanes the SVM is limiting the complexity of the hypothesis space, measured in terms of the VC-dimension (Vapnik and Chervonenkis, 1974).”
Structured Prediction Cascades (2010, methodology): “Standard statistical model selection techniques (Mallows, 1973; Vapnik and Chervonenkis, 1974; Akaike, 1974; Devroye et al., 1996; Barron et al., 1999; Bartlett et al., 2002) explore a hierarchy of models of increasing complexity primarily to minimize expected error, without much concern for the…”
Large Scale Machine Learning (2004, methodology): “A formal analysis of this problem has been proposed by Vapnik and Chervonenkis ( 1974 ) and is known as Structural Risk Minimization .”
Optimization Methods for Large-Scale Machine Learning (2016, background): “Structural Risk Minimization An approach for choosing a prediction function that has proved to be widely successful in practice is structural risk minimization [151, 149].”
Oracle inequalities for computationally adaptive model selection (2012, background): “Several approaches to complexity regularization are possible, and an incomplete bibliography includes the papers [28, 16, 25, 5, 11, 20].”
Developing optimal non-linear scoring function for protein design (2004, methodology): “This is related to the novelty detection problem and single-class support vector machine studied in statistical learning theory (Vapnik and Chervonenkis, 1964, 1974; Sch¨olkopf and Smola, 2002).”
Some Extensions of an Inequality of Vapnik and Chervonenkis (2002, background): “The inequality of Vapnik and Chervonenkis states that (see Theorem 5.3 in [18]) if F is a VC-major class of [0, 1] valued functions with dimension d then for all δ > 0 with probability at least 1− δ for all f ∈ F 1 n(Pf)1/2 n ∑ i=1 (Pf − f(xi)) ≤ 2 (1 n log S(2n) + 1 n log 4 δ )1/2 , (1.1) where for”