Improvements to the SMO algorithm for SVM regression

By Shirish K. Shevade, S. Sathiya Keerthi, C. Bhattacharyya, K. Murthy. IEEE Trans. Neural Networks Learn. Syst., 2000

927 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/dc340ca364cac1763c8831970aa89466c07873c1

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Citations in the corpus

Adaptive computation and machine learning (1998, background): “1997), SMO (Platt 1999; Keerthi et al. 1999a; Shevade et al. 1999) and NPA (Keerthi et al.”
Large Scale Machine Learning (2004, methodology): “For instance, Shevade et al. (2000) proposed two modifications of the SMO algorithm”
Training v-Support Vector Regression: Theory and Algorithms (2002, methodology): “Extensions to ǫ-SVR are in, for example, ( Keerthi et al. 2000; Laskov 2002).”
On the convergence of the decomposition method for support vector machines (2001, methodology): “used in [ 12 ], [13], [24] but here we will not get into details.”
Efficient SVM Regression Training with SMO (2002, background): “We also believe that the improvements to SMO described in Keerthi et al. (1999), Shevade et al. (2000) can be adapted to our implementation as well.”
DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach (2014, methodology): “DUET combines mCSM and SDM in a consensus prediction, by consolidating the results of the separate methods in an optimized predictor using Support Vector Machines (SVMs) trained with Sequential Minimal Optimization (17).”
UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information (2016, methodology): “We use improved Sequential Minimal Optimization (SMO) algorithm for parameter estimation introduce in (Shevade et al., 2000).”
Improvements to Platt's SMO Algorithm for SVM Classifier Design (2001, neutral): “We have reported those results in Shevade et al. (1999).”
Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression (2016, neutral): “If there is a very large scale of samples, some fast optimization techniques can also solve the proposed objective functions, such as the sequential minimal optimization (SMO) algorithm [31], the SGD algorithm [32], and other efficient algorithms [37], [38].”
SVMTorch: Support Vector Machines for Large-Scale Regression Problems (2001, methodology): “Note also that Nodelib includes some enhancements compared to SMO which are di erent from those proposed by Shevade et al. (2000). Both these algorithms use an internal cache in order to be able to solve large-scale problems.”
Unsupervised generation of data mining features from linked open data (2012, methodology): “For the transparency international dataset, we have again used, like in the ﬁrst case study, three common regression algorithms, i.e., Linear Regression, the regression variant of SMO, and M5 rules.”
Convergence of a Generalized SMO Algorithm for SVM Classifier Design (2002, methodology): “We believe that extension of the ideas to similar algorithms for regression problems (Shevade et al., 1999; Schölkopf et al., 1998) is possible.”
BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity (2017, methodology): “In this run, we use support vector machines (Chang and Lin, 2011) (SVM) for regression, more speciﬁcally sequential minimal optimization (Shevade et al., 2000) (SMO).”