Advances in Large Margin Classifiers

By Alex Smola, Peter L. Bartlett. 2000

1,072 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c900c66310a29bdb771270bb22440a4cf42958cb

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Citations in the corpus

Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models (2013, background): “During the past 10 years, the importance of this measure of discrimination has been discovered by the machine learning community as a means of assessing predictive validity (Provost and Fawcett, 2001; Ling et al. , 2003).”
Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra (2020, methodology): “The following is not performed as part of our method evaluation, but only when applying the method to biological data: We transform the linear outputs of the DNN into posterior probabilities using Platt calibration 74 .”
Protein homology detection using string alignment kernels (2004, unknown): “For example, Watkins (2000) and Haussler (1999) show that the probability P(x, y) of emitting two strings x and y under a pair HMM model is a valid convolution kernel.”
A tutorial on?-support vector machines (2005, background): “There are theoretical arguments supporting the good generalization performance of the optimal hyperplane ([41], [37], [4], [33], [44]).”
RSVM: Reduced Support Vector Machines (2001, background): “The quadratic term in (1), which is twice the reciprocal of the square of the 2-norm distance 2‖w‖2 between the two bounding planes of (2) in the n-dimensional space of w ∈ Rn for a fixed γ, maximizes that distance, often called the “margin”.”
Kernel methods in machine learning (2007, unknown): “[124]) that minimizing the lp norm of w leads to the maximizing of the margin of separation in the lq norm where 1 p + 1 q = 1.”