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Support Vector Machines in R [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.
Alexandros Karatzoglou +2 more
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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 ...
Carrizosa, Emilio +2 more
core +17 more sources
Deep Features for Training Support Vector Machines [PDF]
Journal of Imaging, 2021 Features play a crucial role in computer vision. Initially designed to detect salient elements by means of handcrafted algorithms, features now are often learned using different layers in convolutional neural networks (CNNs).
Loris Nanni +2 more
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, 2003 In this report we present an introductory overview of Support Vector Machines (SVMs). SVMs are supervised learning machines that can be analysed theoretically using concepts from computational learning theory while being able to achieve good performance ...
1st Computer Science Annual Workshop (CSAW’03) +1 more
core +4 more sources
Robust Classification via Support Vector Machines
Risks, 2022 Classification models are very sensitive to data uncertainty, and finding robust classifiers that are less sensitive to data uncertainty has raised great interest in the machine learning literature.
Alexandru V. Asimit +4 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 ...
null Gyemin Lee, C. Scott
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Support Vector Machines with Quantum State Discrimination
Quantum Reports, 2021 We analyze possible connections between quantum-inspired classifications and support vector machines. Quantum state discrimination and optimal quantum measurement are useful tools for classification problems.
Roberto Leporini, Davide Pastorello
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Support Vector Machines for Predicting Electrical Faults [PDF]
Engineering and Technology Journal, 2014 Support vector machines (SVMs) are a non-probabilistic binary linear classifier in machine learning techniques and are supervised learning algorithms that classify, predict, recognise and analyse patterns.
Tarik Rashid, Salar J. Abdulhameed
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Wavelet Support Vector Machine [PDF]
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2004 An admissible support vector (SV) kernel (the wavelet kernel), by which we can construct a wavelet support vector machine (SVM), is presented. The wavelet kernel is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. The existence of wavelet kernels is proven by results of theoretic analysis.
Li, Zhang, Weida, Zhou, Licheng, Jiao
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IET Systems Biology, 2023 Background Meningiomas are common primary brain tumours, with macrophages playing a crucial role in their development and progression. This study aims to identify module genes correlated with meningioma‐associated macrophages and analyse their ...
Xiaowei Zhang
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