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
Efficient resource provisioning for elastic Cloud services based on machine learning techniques
Rafael Moreno-Vozmediano,Rubén S. Montero,Eduardo Huedo,Ignacio M. Llorente,Ignacio M. Llorente +4 more
TL;DR: A novel predictive auto-scaling mechanism based on machine learning techniques for time series forecasting and queuing theory that aims to accurately predict the processing load of a distributed server and estimate the appropriate number of resources that must be provisioned in order to optimize the service response time and fulfill the SLA contracted by the user.
Journal ArticleDOI
Feature selection via sensitivity analysis of SVM probabilistic outputs
TL;DR: The proposed feature-selection method, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates the importance of a specific feature by computing the aggregate value of the absolute difference of the probabilistic outputs of SVM with and without the feature.
Journal ArticleDOI
Network and Data Integration for Biomarker Signature Discovery via Network Smoothed T-Statistics
Yupeng Cun,Holger Fröhlich +1 more
TL;DR: A technique that integrates network information as well as different kinds of experimental data (here exemplified by mRNA and miRNA expression) into one classifier is proposed, and it is demonstrated that the data integration strategy can improve classification performance compared to using a single data source only.
Journal ArticleDOI
Information Extraction From Remote Sensing Images for Flood Monitoring and Damage Evaluation
Sebastiano B. Serpico,S. Dellepiane,Giorgio Boni,Gabriele Moser,Elena Angiati,Roberto Rudari +5 more
TL;DR: The challenges and the methodological approaches involved in the multidisciplinary combination of image analysis and hydrometeorology are discussed with the purpose of guiding and optimizing the process of information extraction from satellite data according to the requirements of civil protection from floods.
Journal ArticleDOI
Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds
TL;DR: In this paper, the least square support vector machine (SVM) was used to classify different varieties of maize seeds based on spectral, textural, or fusion data, and the resulting classification maps were developed to visualize different maize seeds.
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.