Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
By Vojtech Franc, Sören Sonnenburg. 2009
86 citations recorded by the source corpus.
Subfields: Computational Mechanics, Industrial and Manufacturing Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Suvrit Sra
- Veronica Piccialli
- Marco Sciandrone
- Daniel Quang
- Xiaohui S. Xie
- Yifei Chen
- Fei Han
- Christopher M. Reardon
In Industrial and Manufacturing Engineering
- Veronica Piccialli
- Marco Sciandrone
- Daniel Quang
- Xiaohui S. Xie
- Yifei Chen
- Fei Han
- Christopher M. Reardon
- Hao Zhang
Citations in the corpus
- CADD: predicting the deleteriousness of variants throughout the human genome (2018, positive): “0 used a linear support vector machine implemented in the LIBOCAS library (42), later models used L 2 -regularized logistic regression implemented in GraphLab Create (43).”
- A general framework for estimating the relative pathogenicity of human genetic variants (2014, neutral): “96 library 21 .”
- Nonlinear optimization and support vector machines (2018, methodology): “…is defined for regularized risk minimization problems, that is shown to converge in O ( 1 /(cid:13)) steps for linear classification problems, and that is further optimized in Franc and Sonnenburg (2009) and Franc and Sonnenburg (2008), where an optimized choice of the cutting planes is described.”
- The SHOGUN Machine Learning Toolbox (2010, methodology): “Keywords: support vector machines, kernels, large-scale learning, Python, Octave, R”
- Minimum uncertainty latent variable models for robot recognition of sequential human activities (2017, methodology): “This problem can be solved using the BCP algorithm [18] that is based on the cutting plane technique [27].”
- DANN: a deep learning approach for annotating the pathogenicity of genetic variants (2015, methodology): “97 library (Franc and Sonnenburg, 2009) with parameter C ¼ 0.0025, closely replicating CADD’s training.”
- Optimization for machine learning (2010, background): “It was found experimentally (Franc and Sonnenburg, 2010), that value θ = 0.”
- A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning (2008, background): “Recently they have extended OCAS to multiclass classification (Franc and Sonnenburg, 2009).”