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
More filters
Journal ArticleDOI
Multisensor of thermal and visual images to detect concealed weapon using harmony search image fusion approach
TL;DR: A novel approach to detect concealed weapons based on discrete wavelet transform in conjunction with dimension reduced meta-heuristic algorithm, the harmony search, and shape matching based K means SVM classification is presented.
Book ChapterDOI
Power transformer fault diagnosis using support vector machines and artificial neural networks with clonal selection algorithms optimization
TL;DR: This paper presents an innovative method based on Artificial Neural Network and multi-layer Support Vector Machine for the purpose of fault diagnosis of power transformers and a clonal selection algorithm (CSA) based encoding technique is applied to improve the accuracy of classification.
Posted Content
Classification of glomerular hypercellularity using convolutional features and support vector machine
Paulo Chagas,Luiz Enrique Vieira de Souza,Ikaro Araújo,Nayze Lucena Sangreman Aldeman,Angelo Duarte,Michele Fúlvia Angelo,Washington Luis Conrado dosSantos,Luciano Oliveira +7 more
TL;DR: In this paper, a convolutional neural network (CNN) along with a support vector machine (SVM) was used for classification of glomerular hypercellularity in human kidney images.
Journal ArticleDOI
Variable Selection for Support Vector Machines
Surette Bierman,Sarel J. Steel +1 more
TL;DR: A new two-step approach to variable selection for SVMs is proposed: best variable subsets of size k = 1,2,…, p are first identified, and then a new data-dependent criterion is used to determine a value for m.
Journal ArticleDOI
The Differential Diagnosis of Multiple Sclerosis Using Convex Combination of Infinite Kernels.
TL;DR: The calculations show that the proposed model classifies the multiple sclerosis (MS) diagnosis level with better accuracy than single kernel, artificial neural network and other machine learning methods, and it can also be used as a decision support system for identifying MS health status of patients.
References
More filters
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Journal ArticleDOI
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.