Results 11 to 20 of about 427,183 (276)
Quantum Support Vector Machines for Aerodynamic Classification
Intelligent Computing, 2023 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
doaj +1 more source
Faster Support Vector Machines [PDF]
ACM Journal of Experimental Algorithmics, 2019 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
On Subsampling Procedures for Support Vector Machines
Mathematics, 2022 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
Directional Support Vector Machines
Applied Sciences, 2019 Several phenomena are represented by directional—angular or periodic—data; from time references on the calendar to geographical coordinates.
Diogo Pernes +2 more
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Robust ASR using Support Vector Machines [PDF]
, 2007 The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important
A. Gallardo-Antolín +27 more
core +7 more sources
Study on Privacy-preserving Nonlinear Federated Support Vector Machines [PDF]
Jisuanji kexue, 2022 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
doaj +1 more source
Support Vector Machines in High Energy Physics [PDF]
, 2008 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
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Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training [PDF]
, 2018 We consider the convex quadratic linearly constrained problem with bounded variables and with huge and dense Hessian matrix that arises in many applications such as the training problem of bias support vector machines.
Andrea Manno +2 more
core +1 more source
Spatio-temporal avalanche forecasting with Support Vector Machines [PDF]
Natural Hazards and Earth System Sciences, 2011 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
EURASIP Journal on Advances in Signal Processing, 2008 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