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
Framelet Kernels With Applications to Support Vector Regression and Regularization Networks
TL;DR: This paper presents a new class of kernel functions derived from the framelet system, which has the ability to approximate functions with a multiscale structure and can reduce the influence of noise in data.
Proceedings ArticleDOI
A majorization-minimization algorithm for (multiple) hyperparameter learning
TL;DR: A general Bayesian framework for hyperparameter tuning in L2-regularized supervised learning models is presented, and a local optimum of the resulting non-convex optimization problem is found efficiently using a majorization-minimization (MM) algorithm.
Journal ArticleDOI
Support Vector Machine Polyhedral Separability in Semisupervised Learning
TL;DR: This model aims at separating two finite and disjoint sets of points by means of a polyhedral surface in the semisupervised case, that is, by exploiting information coming from both labeled and unlabeled samples.
Journal ArticleDOI
Prediction of Daily Dewpoint Temperature Using a Model Combining the Support Vector Machine with Firefly Algorithm
Eiman Tamah Al-Shammari,Kasra Mohammadi,Afram Keivani,Siti Hafizah Ab Hamid,Shatirah Akib,Shahaboddin Shamshirband,Dalibor Petković +6 more
TL;DR: It is found that further precision is achieved for Model 7 established based on all approaches utilizing three inputs of Tavg, Rh, and P, which indicates that the SVM-FFA method, by providin...
Journal ArticleDOI
Computational Intelligence Techniques for Tactile Sensing Systems
TL;DR: The research applies novel computational intelligence techniques and a tensor-based approach for the classification of touch modalities and results consist in providing a procedure to enhance system generalization ability and architecture for multi-class recognition applications.
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.