Fast training of support vector machines using sequential minimal optimization, advances in kernel methods

By John C. Platt. 1999

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Citations in the corpus

Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, background): “– a set of hidden states Q = q 1 , q 2 , ..., q m – a transition probability matrix”
Stochastic dual coordinate ascent methods for regularized loss (2012, positive): “The above theorem applies to the hinge-loss function, φ i ( u ) = max { 0 , 1 − y i a } .”
Learn to Solve Algebra Word Problems Using Quadratic Programming (2015, methodology): “This is a QP problem and has been well studied (Platt, 1999; Fan et al., 2008).”
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 .”
Improvements to Platt's SMO Algorithm for SVM Classifier Design (2001, methodology): “(Complete details are given in Platt, 1998.)”
Automatically Constructing a Corpus of Sentential Paraphrases (2005, methodology): “(Vapnik, 1995), in this case an implementation of the Sequential Minimal Optimization (SMO) algo-rithm described in Platt (1999), 2 which has been shown to be useful in text classification tasks (Dumais 1998; Dumais et al., 1998).”
A study of the effect of different types of noise on the precision of supervised learning techniques (2010, methodology): “We have used the versions of these algorithms which are available in Weka Version 3.5.5 (Hall et al. 2009): (1) Naive Bayes probabilistic classifier (John and Langley 1995); (2) C4.5 decision tree (Quinlan 1993); (3) IBk instance-based algorithm (Aha et al. 1991 )a nd (4) SMO ( Platt 1998 ) support ”
CRITICAL ASSESSMENT OF AUTOMATED FLOW CYTOMETRY DATA ANALYSIS TECHNIQUES (2013, methodology): “We have used the SMO algorithm [43] implemented at WEKA [44] to train the SVM machine.”
Support Vector Regression (2007, methodology): “Platt [41] has proposed a new algorithm for training support vector machines: Sequential Minimal Optimization (SMO) which is a simple algorithm that can quickly solve the SVM QP (Support Vector Machine Quadratic Programming) problems without any extra matrix storage and without using numerical QP op”
Man vs. Machine: Practical Adversarial Detection of Malicious Crowdsourcing Workers (2014, methodology): “We utilize a number of popular algorithms widely used in security contexts, including two Bayesian methods: Naive Bayesian (NB) [20] and BayesNet (BN) [18]; two Support Vector Machine methods [33]: SVM with radial basis function kernel (SVMr) and SVM with polynomial kernel (SVMp); and two Tree-based”
Support Vector Machines: Theory and Applications (2001, unknown): “A sequential optimization method has also recently been proposed [ 9 ].”
Recent Advances in Techniques for Hyperspectral Image Processing (2009, methodology): “…and that exhibited by standard SVMs when images associated to larger areas are considered, 1 The sequential minimal optimization (SMO) algorithm (Platt, 1999) was used in the learning phase of both the standard SVM and the proposed TSVM (making proper modifications), thus exploiting the…”
Duality and Geometry in SVM Classifiers (2000, background): “, 1999) and a minor variant of sequential minimal optimization (SMO) (Platt, 1998).”
Trust region Newton methods for large-scale logistic regression (2007, methodology): “a9a: This set is compiled in (Platt, 1998) from the UCI “adult” data set (Newman et al.”