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
Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm
TL;DR: A port throughput forecasting scheme is proposed that hybridizes the RSVR, CSAPSO and MARS to obtain a more accurate forecasting result and the experimental results indicate that the proposed scheme obtains better forecasting result than the six competing models in terms of forecasting error.
Journal ArticleDOI
A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence
TL;DR: A new hybrid approach for feature selection and Support Vector Machine (SVM) model selection based on a new variation of Cohort Intelligence (CI) algorithm, SACI, which outperformed CI and comparable to or better than the other compared metaheuristics in terms of the SVM classification accuracy and dimensionality reduction.
Journal ArticleDOI
Multiclass Feature Selection With Kernel Gram-Matrix-Based Criteria
TL;DR: It is demonstrated how two criteria designed for the optimization of SVM: kernel target alignment and kernel class separability can build efficient and simple methods, easily applicable to multiclass problems and iteratively computable with minimal memory requirements.
Journal ArticleDOI
Estimation of Crowd Density Based on Wavelet and Support Vector Machine
TL;DR: Compared with the conventional statistical techniques and wavelet energy techniques used in single-scale images, the proposed algorithm can achieve much improved performance and more detailed information of the crowd density can be captured by the new feature extraction method.
Journal ArticleDOI
A dynamic model selection strategy for support vector machine classifiers
TL;DR: This paper proposes a strategy to select optimal SVM models in a dynamic fashion in order to address this problem when knowledge about the environment is updated with new observations and previously parameterized models need to be re-evaluated, and in some cases discarded in favor of revised models.
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.