The Nature Of Statistical Learning Theory

By Vladimir Cherkassky. 1997

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Citations in the corpus

A hybrid support vector regression framework for streamflow forecast (2019, methodology): “Support vector machine (SVM), which is known as classification and then extended for regression, was proposed by (Vapnik, 1995).”
Application of Gaussian Process Regression Model to Predict Discharge Coefficient of Gated Piano Key Weir (2019, methodology): “The SVM is a supervised learning technique which was introduced by Vapnik (1995) and is based on statistical learning theory (SLT)(Vapnik 2013).”
Estimation of the forgetting factor in kernel recursive least squares (2012, methodology): “Recently, a class of kernel-based adaptive filter algorithms have emerged [3, 4], following the success of kernel methods such as the support vector machine and kernel principal component analysis [5, 6].”
Deep learning for cardiovascular medicine: a practical primer. (2019, background): “61 Cardiovascular medicine, in particular, is well suited to benefit from smart analysis of continuous and massive data streams in this new era of wearable sensors, to integrate traditional health data with lifestyle indices ( Take home figure ).”
Landslide susceptibility evaluation and hazard zonation techniques – a review (2020, background): “aiming to find a linear hyperplane that separates two classes optimally (Vapnik 1999).”
Comparing machines and humans on a visual categorization test (2011, methodology): “We have used two popular machine learning algorithms in our experiments: boosting with the standard Adaboost procedure (5) and a support vector machine with a Gaussian kernel (6).”
Kernel Logistic Regression and the Import Vector Machine (2001, background): “Various misclassification error bounds have been derived based on the margin (e.g., Vapnik 1995, 1998; Bartlett and Shawe-Taylor 1999; Shawe-Taylor and Cristianini 1999).”
An Information-Theoretic View for Deep Learning (2018, background): “For example, the VC dimension (Vapnik 2013), covering number (Zhang 2002), Rademacher (Bartlett and Mendelson 2002, Bartlett et al.”
Pricing American Options: A Duality Approach (2018, methodology): “Possible architectures are the linearly parametrized architectures of Longstaff and Schwartz (2000) and Tsitsiklis and Van Roy (2000), or non-linearly parametrized architectures such as neural networks or support vector machines (see Vapnik 1999).”