Least Squares Support Vector Machine Classifiers

By Johan A. K. Suykens, Joos Vandewalle. 1999

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Citations in the corpus

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics (2015, neutral): “Finally, we classify these protein families using support vector machine (SVM) classifiers [5].”
Making AI Forget You: Data Deletion in Machine Learning (2019, methodology): “Now we consider support vector machines [68], another canonical machine learning algorithm.”
PIXOR: Real-time 3D Object Detection from Point Clouds (2018, methodology): “Hand-crafted geometry features are extracted on each volume and fed into an SVM classifier [34].”
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection (2018, methodology): “We compare the above settings with six clas-siﬁcation models: Support Vector Machine (SVM) (Suykens and Vandewalle, 1999), Logistic Regression, attentional BI-LSTM, two CNN models by Kim (2014), and a N-gram model (Waseem and Hovy, 2016).”
A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system (2015, unknown): “LS-SVM is the least squares version of support vector machines (SVMs), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis [71].”
Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals (2015, background): “For a set of data points {xk, yk} K k=1, xk ∈ R n is the input data and yk ∈ {+1,−1} is the k-th output class label, the discriminant function can be defined as [57]: v(x) = sign [ W u(x) + β ]”
Time Series Prediction Using Support Vector Machines: A Survey (2009, background): “The details of the LS-SVM development can be found in [9] [10].”
Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities (2017, unknown): “The widely used surrogate models in UQ studies include Gaussian process model (Kriging model) [102], polynomial chaos expansion (PCE) [15], support vector machine (SVM) [103], and neural networks.”
Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting (2012, methodology): “Therefore, least squares support vector regression (LSSVR), proposed by Suykens and Vandewalle (1999), has been applied in much literature as an alternative (He et al., 2010; Khemchandani et al., 2009); it is a simplified version of traditional SVR that alters inequality constraints into equal…”
Kernel-based methods for hyperspectral image classification (2005, background): “on the theoretical relation between KFD and least squares SVMs (LS-SVMs) [43], since it allows embedding learning”
What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle (2015, methodology): “(Refer to Table I for detail comparisons between ELM and SVM / LSSVM, and Huang, et al. [6], [23] for detail analysis on the reasons why SVM and LS-SVM provide suboptimal solutions in general.)”
Nondestructive Measurement of Soluble Solids Content of Kiwifruits Using Near-Infrared Hyperspectral Imaging (2015, unknown): “…more compacted supported kernel, RBF could handle the nonlinear relationships between the spectra and target attributes well, can give a good performance under general smoothness assumptions, and is able to reduce the computational complexity of the training procedure (Suykens and Vandewalle 1999).”
Quantification of Nitrogen Status in Rice by Least Squares Support Vector Machines and Reflectance Spectroscopy (2009, background): “LS-SVM can work with linear or non-linear regression or multivariate function estimation in a relatively fast way (Suykens and Vanderwalle 1999; Chen et al. 2007).”