Choosing Multiple Parameters for Support Vector Machines
TLDR
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters.Abstract:
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.read more
Citations
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Journal ArticleDOI
A trainable feature extractor for handwritten digit recognition
TL;DR: A trainable feature extractor based on the LeNet5 convolutional neural network architecture is introduced to solve the first problem in a black box scheme without prior knowledge on the data and the results show that the system can outperform both SVMs and Le net5 while providing performances comparable to the best performance on this database.
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Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models
Shaobo Huang,Ning Fang +1 more
TL;DR: The research findings from the present study imply that if the goal of the instructor is to predict the average academic performance of his/her dynamics class as a whole, the instructor should choose the simplest mathematical model, which is the multiple linear regression model, with student's cumulative GPA as the only predictor variable.
Journal ArticleDOI
Fault Classification and Section Identification of an Advanced Series-Compensated Transmission Line Using Support Vector Machine
TL;DR: The proposed method converges very fast with fewer numbers of training samples compared to neural-network and neuro-fuzzy systems which indicates fastness and accuracy of the proposed method for protection of the transmission line with TCSC.
Journal ArticleDOI
A support vector machine–firefly algorithm-based model for global solar radiation prediction
Lanre Olatomiwa,Lanre Olatomiwa,Saad Mekhilef,Shahaboddin Shamshirband,Kasra Mohammadi,Dalibor Petković,Ch. Sudheer +6 more
TL;DR: In this article, a hybrid machine learning technique for solar radiation prediction based on some meteorological data is examined, which is developed by hybridizing the Support Vector Machines (SVMs) with Firefly Algorithm (FFA) to predict the monthly mean horizontal global solar radiation using three meteorological parameters of sunshine duration (n¯), maximum temperature (Tmax), and minimum temperature(Tmin) as inputs.
Proceedings ArticleDOI
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
TL;DR: To encode the geometry of the manifold in the mapping, a family of provably positive definite kernels on the Riemannian manifold of SPD matrices is introduced, derived from the Gaussian kernel, but exploit different metrics on the manifold.
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