SVMTorch: Support Vector Machines for Large-Scale Regression Problems

By Ronan Collobert, Samy Bengio. Journal of machine learning research, 2001

1,015 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/35cf1e2f3b193122e91d18a5232c4c530b696ba8

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Citations in the corpus

Supervised classification and mathematical optimization (2013, methodology): “State-of-the-art software for SVM includes libraries such as LIBSVM [54], SVMTorch [64] and SVMlight [127].”
Support vector machines for wind speed prediction (2004, unknown): “[7–11].”
Support vector machines using GMM supervectors for speaker verification (2006, methodology): “Both kernels in (8) and (12) were implemented using SVMTorch as an SVM trainer [7].”
Reinforcement Learning as Classification: Leveraging Modern Classifiers (2003, methodology): “The SVMTorch package provides a simple multiclass capability (one versus all), but is not necessarily representative of the best that can be done on multiclass problems using SVM technology.”
A Parallel Mixture of SVMs for Very Large Scale Problems (2001, background): “Note however that current state-of-the-art algorithms appear to have training time complexity scaling much closer to O(N 2 ) than O(N 3 ) Collobert & Bengio, 2001; Joachims, 1999; Platt, 1999 .”
Scaling Large Learning Problems with Hard Parallel Mixtures (2002, background): “(2) Train each expert Pi(Y |X) separately over Di.”
The kernel recursive least-squares algorithm (2004, methodology): “We assume that a standard preprocessing step has been Table 1: The Kernel RLS Algorithm.”
The Need for Open Source Software in Machine Learning (2007, methodology): “A typical example are the libraries for learning support vector machines, such as LIBSVM (Chang and Lin, 2001), SVMLin (Sindhwani and Keerthi, 2006), SVMTorch (Collobert and Bengio, 2001) and GPDT (Zanni et al.”
Feature Extraction by Non-Parametric Mutual Information Maximization (2003, methodology): “A Support Vector Machine (SVM) implementation, called SVMTorch, was used as a second classifier (Collobert and Bengio, 2001).”
Random-Walk Term Weighting for Improved Text Classification (2007, methodology): “In our evaluations we use SVMTorch [5] with a linear kernel, since it was proved to be as powerful as other kernels in text classification experiments [28].”
Classifying large data sets using SVMs with hierarchical clusters (2003, methodology): “Chunking, decomposition [13, 7], and sequential minimal op timization [18] are most well-known techniques.”
Using discriminant analysis for multi-class classification: an experimental investigation (2006, unknown): “SVMTorch ( Collobert & Bengio , 2001 ) 9 is used for experiments involving SVMs .”
Nondestructive Detection of Soluble Solids Content of Apples from Dielectric Spectra with ANN and Chemometric Methods (2015, background): “Detailed information on SVM can be found elsewhere (Collobert and Bengio 2001; Furey et al. 2000).”
Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, methodology): “1996), support vector machine (Collobert and Bengio 2001), etc.”
SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation (2006, methodology): “The vectors x i are support vectors and obtained from the training set by an optimization process [6].”
A Meta-Learning Method to Select the Kernel Width in Support Vector Regression (2004, methodology): “Performance of SVMTorch was estimated by 10-fold cross-validation.2 Let us examine how the error varies as the kernel width is varied for some data sets.”
Mining newsgroups using networks arising from social behavior (2003, methodology): “For SVM, we used Ronan Collobert’s SVMTorc h II [7].”