Results 11 to 20 of about 469,122 (324)
Quantum Support Vector Machines for Aerodynamic Classification
Aerodynamics plays an important role in the aviation industry and aircraft design. Detecting and minimizing the phenomenon of flow separation from scattered pressure data on the airfoil is critical for ensuring stable and efficient aviation.
Xi-Jun Yuan +7 more
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Semismooth support vector machines [PDF]
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ferris, Michael C., Munson, Todd S.
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On Subsampling Procedures for Support Vector Machines
Herein, theoretical results are presented to provide insights into the effectiveness of subsampling methods in reducing the amount of instances required in the training stage when applying support vector machines (SVMs) for classification in big data ...
Roberto Bárcenas +3 more
doaj +1 more source
Faster Support Vector Machines [PDF]
The time complexity of support vector machines (SVMs) prohibits training on huge datasets with millions of data points. Recently, multilevel approaches to train SVMs have been developed to allow for time-efficient training on huge datasets.
Sebastian Schlag +2 more
openaire +6 more sources
Directional Support Vector Machines
Several phenomena are represented by directional—angular or periodic—data; from time references on the calendar to geographical coordinates.
Diogo Pernes +2 more
doaj +1 more source
Study on Privacy-preserving Nonlinear Federated Support Vector Machines [PDF]
Federated learning offers new ideas for solving the problem of multiparty joint modeling in “data silos”.Federated support vector machines can realize cross-device support vector machine modeling without local data,but the existing research has some ...
YANG Hong-jian, HU Xue-xian, LI Ke-jia, XU Yang, WEI Jiang-hong
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Robustness Verification of Support Vector Machines [PDF]
We study the problem of formally verifying the robustness to adversarial examples of support vector machines (SVMs), a major machine learning model for classification and regression tasks.
A Miné +25 more
core +2 more sources
Support Vector Machines in High Energy Physics [PDF]
This lecture will introduce the Support Vector algorithms for classification and regression. They are an application of the so called kernel trick, which allows the extension of a certain class of linear algorithms to the non linear case.
Vossen, Anselm
core +2 more sources
Spatio-temporal avalanche forecasting with Support Vector Machines [PDF]
This paper explores the use of the Support Vector Machine (SVM) as a data exploration tool and a predictive engine for spatio-temporal forecasting of snow avalanches.
A. Pozdnoukhov +3 more
doaj +1 more source
Sparse Deconvolution Using Support Vector Machines
Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them ...
Aníbal R. Figueiras-Vidal +5 more
doaj +1 more source

