A nested heuristic for parameter tuning in Support Vector Machines

By Emilio Carrizosa, Belén Martín-Barragán, Dolores Romero Morales. 2014

32 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics

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Citations in the corpus

Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, methodology): “The tuning procedure works as follows, (Carrizosa et al. 2014, Carrizosa and Romero Morales 2013).”
Clustering Categories in Support Vector Machines (2017, background): “, n (3) ω ∈ R ∑J j=1Kj (4) ω′ ∈ R ′ (5) b ∈ R, (6) where (ξi) denotes the vector of deviation variables and the parameter denoted by C is a nonnegative regularization parameter that calls for tuning, [6, 8].”
Global optimization issues in deep network regression: an overview (2018, methodology): “Also in [35] an heuristic based on Variable Neighborhood Search has been proposed to address the problem of kernels selection.”
Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm (2019, unknown): “here ω is a scalar bandwidth to be tuned, ( Carrizosa et al., 014; Carrizosa & Romero Morales, 2013; Cristianini & Shaweaylor, 20 0 0; Hofmann, Schölkopf, & Smola, 2008; Keerthi & Lin, 003 ). In this paper, for simplicity, we only focus on the Gaussian ernel, one of the most used and effective kerne”
GOTCHA! Network-Based Fraud Detection for Social Security Fraud (2017, methodology): “For reasons of generality , we use the term Automated Detection Algorithms to refer to any technique that is able to estimate a fraud detection model, such as tree models, linear or logistic functions, SVMs, ANNs, Bayesian learning, ensemble models, etc. (Hastie et al. 2001, Carrizosa et al. 2014).”
Selection of time instants and intervals with Support Vector Regression for multivariate functional data (2020, methodology): “…Carrizosa and Romero Morales (2013), Cristianini and Shawe-Taylor (2000) and Hofmann et al. (2008), the polynomial kernel, Muñoz and González (2010) and Rossi and Villa (2006), or the Gaussian (RBF) kernel, Carrizosa et al. (2014), Cristianini and Shawe-Taylor (2000) and Keerthi and Lin (2003).”