Results 11 to 20 of about 9,444,467 (301)
Support Vector Machines inR [PDF]
Journal of Statistical Software, 2006 Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations.
Karatzoglou, Alexandros +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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The Stata Journal: Promoting communications on statistics and Stata, 2016 Support vector machines are statistical- and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines.
Guenther, Nick, Schonlau, Matthias
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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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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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Binarized Support Vector Machines [PDF]
INFORMS Journal on Computing, 2010 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 interpretability, which is an important issue in data mining.In this work, we propose an SVM-based method that ...
Emilio Carrizosa +2 more
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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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A colour-based building recognition using support vector machine [PDF]
, 2019 Many applications apply the concept of image recognition to help human in recognising objects simply by just using digital images. A content-based building recognition system could solve the problem of using just text as search input.
Yee, Loh Weng; Universiti Putra Malaysia +3 more
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Turbopump Condition Monitoring Using Incremental Clustering and One-class Support Vector Machine [PDF]
, 2011 : Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine (LRE). Because of lack of fault samples, a monitoring system cannot be trained on all possible condition patterns.
Gu, Fengshou +7 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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