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TV-SVM: Support Vector Machine with Total Variational Regularization
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018It is required that input features are represented as vectors or scalars in machine learning for classification, e.g. support vector machine (SVM). However, real world data such as 2D images is naturally represented as matrices or tensors with higher dimensions.
Zhendong Zhang 0001, Cheolkon Jung
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Diagnosis Using Support Vector Machines (SVM)
2016Diagnosis of functional failures at the board level is critical for improving product yield and reducing manufacturing cost. State-of-the-art board-level diagnostic software is unable to cope with high complexity and ever-increasing clock frequencies, and the identification of the root cause of failure on a board is a major problem today.
Fangming Ye +3 more
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Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis
Applied Soft Computing, 2020Support vector machine (SVM) is a widely used pattern classification method that its classification accuracy is greatly influenced by both kernel parameter setting and feature selection.
Mingjing Wang, Huiling Chen
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2009
Support vector machines (SVMs), including support vector classifier (SVC) and support vector regressor (SVR), are among the most robust and accurate methods in all well-known data mining algorithms. SVMs, which were originally developed by Vapnik in the 1990s [1-11], have a sound theoretical foundation rooted in statistical learning theory, require only
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Support vector machines (SVMs), including support vector classifier (SVC) and support vector regressor (SVR), are among the most robust and accurate methods in all well-known data mining algorithms. SVMs, which were originally developed by Vapnik in the 1990s [1-11], have a sound theoretical foundation rooted in statistical learning theory, require only
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Target recognition in SAR images with Support Vector Machines (SVM)
2007 IEEE International Geoscience and Remote Sensing Symposium, 2007This paper addresses object recognition problem in SAR images with SVM classifier; the work has been mainly focused on feature vector definition. Actually, each object is represented by a feature vector and SVM aims to estimate the best hyperplanes that separate classes in the feature space.
Céline Tison +2 more
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Hardware Support Vector Machine (SVM) for satellite on-board applications
2014 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 2014Since their introduction in 1995, Support Vector Machines (SVM) have shown that classification by this relatively recent machine learning tool can be more accurate than popular contemporary techniques such as neural networks and decision trees, hence causing it to find its way quickly to various applications in engineering, economy and statistics ...
Abdul-Halim M. Jallad, Lubna B. Mohammed
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SN-SVM: a sparse nonparametric support vector machine classifier
Signal, Image and Video Processing, 2012This paper introduces a novel sparse nonparametric support vector machine classifier (SN-SVM) which combines data distribution information from two state-of-the-art kernel-based classifiers, namely, the kernel support vector machine (KSVM) and the kernel nonparametric discriminant (KND).
Naimul Mefraz Khan +3 more
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Optimization of support vector machine (SVM) for object classification
SPIE Proceedings, 2012The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used ...
Matthew Scholten +3 more
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Asymptotic efficiency of kernel support vector machines (SVM)
Cybernetics and Systems Analysis, 2009The paper analyzes the asymptotic properties of Vapnik's SVM-estimates of a regression function as the size of the training sample tends to infinity. The estimation problem is considered as infinite-dimensional minimization of a regularized empirical risk functional in a reproducing kernel Hilbert space.
V. I. Norkin, M. A. Keyzer
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