Least Squares Support Vector Machines
By Rohan Shah
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Citations in the corpus
- Interval Coded Scoring: a toolbox for interpretable scoring systems (2018, background): “Naive Bayes and linear and nonlinear (gaussian kernel) Least-Squares Support Vector Machines (Suykens et al., 2002) The labelling distinguishes between 1655 normal, 295 suspected and 176 pathological records.”
- Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters (2007, background): “A more efficient training algorithm can be obtained, taking advantage of the special structure of the system of linear equations (Suykens et al., 2002).”
- Evaluating the Unloading Gradient Pressure in Continuous Gas-lift Systems During Petroleum Production Operations (2014, methodology): “…y1) , . . . , (xm,ym)}n×, where each output yi ∈ and the input xi ∈, LSSVM for regression problem is introduced as minimization of following formula (Suykens et al., 2002; Cawley and Talbot, 2007): min J (w, ξ ) = 1 − 2w2 + 1 2μ m∑ i=1 (ξi) 2 (1) s.t. : yi = wT ϕ (xi) + b + ξi, i = 1, 2, . . .…”
- Universal Approximation Capability of Broad Learning System and Its Structural Variations (2019, background): “The cost parameter C and kernel parameter γ of SVM, LSSVM [26], and ELM [27] play an important role in learning a good regression model, hence they have to be chosen appropriately for a fair comparison.”
- Multicategory Proximal Support Vector Machine Classifiers (2005, methodology): “For a comprehensive approach to the related least squares support vector machines see [30], where geometric and statistical interpretations as well as the link with the Fischer discriminant analysis are given.”
- Comparison of two optimized machine learning models for predicting displacement of rainfall-induced landslide: A case study in Sichuan Province, China (2017, methodology): “This algorithm can be implemented in MATLAB with a combination of the GAOT toolbox (Houck et al., 1995) and LSSVM toolbox (Suykens et al., 2002).”
- Safety in numbers: Learning categories from few examples with multi model knowledge transfer (2010, methodology): “(1) It can be shown [20] that the optimal w is expressed by w = l i=1 α i φ(x i ), and (α α α, b) are found solving K + 1 C I 1 1 T 0 α α α b = y 0 , (2) where K is the kernel matrix.”
- Multi-View Clustering and Feature Learning via Structured Sparsity (2013, methodology): “…of the same set of objects could give rise to different kernel functions, thus the Multiple Kernel Learning (MKL) approaches (Yu et al., 2010; Suykens et al., 2002; Kloft et al.; Ye et al., 2008a; Lanckriet et al., 2004a; Bach et al., 2004; Sonnenburg et al., 2006) have recently become very…”
- Modelling beyond regression functions: an application of multimodal regression to speed–flow data (2006, methodology): “…regimes, where no prior assignment of points to regimes is required; the tool that is used for estimation is the support vector machine; see also Suykens et al. (2002) for related contributions from the machine learning community); (c) switching regression (Quandt, 1972); (d) Bayesian…”
- Nondestructive Differentiation of Panax Species Using Visible and Shortwave Near-Infrared Spectroscopy (2011, methodology): “The LS-SVM has the capability for linear and nonlinear multivariate calibration and solves the multivariate calibration problems in a relatively fast way ( Suykens et al. 2002 ).”
- Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows (2013, unknown): “LS-SVM is a simplified version of the original SVM algorithm for classification and function estimation, which maintains the advantages and the attributes of the original SVM theory (Suykens et al ., 2002).”
- What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle (2015, background): “[45]) Universal approximation and classification capability Proved theoretically for wide types of nonlinear piecewise nodes / neurons No theoretical proof No theoretical proof”
- Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification (2008, methodology): “manding set of linear equations than a QP problem [29].”
- Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach (2018, methodology): “The selection of the most important affecting factors for groundwater potential mapping (GWPM) using the LSSVM method are shown in Fig.”