Results 11 to 20 of about 108,016 (310)
Binarized support vector machines [PDF]
INFORMS Journal on Computing, 2007 The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their ...
Martin-Barragan, Belen +2 more
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On Coresets for Support Vector Machines [PDF]
Theoretical Computer Science, 2020 We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a models trained on the coreset are provably competitive with those trained on the original data set. Since the size of the
Murad Tukan +3 more
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Properties of Support Vector Machines [PDF]
Neural Computation, 1998 Support vector machines (SVMs) perform pattern recognition between two point classes by finding a decision surface determined by certain points of the training set, termed support vectors (SV). This surface, which in some feature space of possibly infinite dimension can be regarded as a hyperplane, is obtained from the solution of a problem of ...
PONTIL M, VERRI, ALESSANDRO
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Nested support vector machines [PDF]
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 The one-class and cost-sensitive support vector machines (SVMs) are state-of-the-art machine learning methods for estimating density level sets and solving weighted classification problems, respectively. However, the solutions of these SVMs do not necessarily produce set estimates that are nested as the parameters controlling the density level or cost ...
Gyemin Lee, Clayton Scott
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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
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Chunking with support vector machines [PDF]
Second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies 2001 - NAACL '01, 2001 We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted
KUDO, TAKU, MATSUMOTO, YUJI
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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
F. Díaz-de-María +17 more
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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
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Covering Numbers for Support Vector Machines [PDF]
, 1999 Support vector (SV) machines are linear classifiers that use the maximum margin hyperplane in a feature space defined by a kernel function. Until recently, the only bounds on the generalization performance of SV machines (within Valiant’s probably ...
Shawe-Taylor, John +7 more
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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
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