Support vector machines: hype or hallelujah?
By Kristin P. Bennett, Colin Campbell. SKDD, 2000
766 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/62bb14cc7c1a3c0841262846b3eb55a5da43df1c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Emilio Carrizosa
- Dolores Romero Morales
- Michalis Faloutsos
- K. Claffy
- Brian Litt
- Diane Joyce Cook
- Jian Hou
- Javier M. Moguerza
In Computational Theory and Mathematics
- Isabel Santana
- Dina Silva
- Brian Litt
- Jian Hou
- Manuela Guerreiro
- Javier M. Moguerza
- Ana Rodrigues
- Alberto Muñoz
Citations in the corpus
- Automatic Video Classification: A Survey of the Literature (2008, background): “well-suited to classification problems in which there are few training examples but the feature vectors have many terms [26].”
- Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, neutral): “The mathematical algorithms for discrete classification include linear discriminant analysis (LDA) (259, 288, 629), support vector machine (56, 288, 780), ANNs (359), multilayer perceptron (43, 75), hidden Markov models (603, 655), k nearest neighbors classifier (177), and nonlinear Bayesian classif”
- Support Vector Machines with Applications (2006, background): “ppeared. A reference with many historical annotations is the book by Cristianini and Shawe-Taylor [20]. For a review of SVMs from a purely geometrical point of view, the paper by Bennett and Campbell [9] is advisable. An exposition of kernel methods with a Bayesian taste can be read in the book by H”
- One-Class Novelty Detection for Seizure Analysis from Intracranial EEG (2006, methodology): “Several excellent tutorials provide historical context and details on the SVM (Burges, 1998; Bennett & Campbell, 2000; Müller et al., 2001).”
- Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters (2010, neutral): “A short overview of SVM can be found in Bennett and Campbell (2003).”
- Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests (2011, methodology): “Thus, the above minimization is generally solved through a dual formulation problem [see e.g. [41,43]]: Where a i ( i = 1,..., n ) are nonnegative Lagrange multipliers and K(.) is a kernel unction.”
- Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes (2016, methodology): “two types of methods suitable to accomplish this task [5].”
- Internet traffic classification demystified: myths, caveats, and the best practices (2008, methodology): “The SVM had already been shown to perform well in multiple areas of various pattern classification problems [8].”