New Support Vector Algorithms
Reads0
Chats0
TLDR
A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.Abstract:
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of ν, and report experimental results.read more
Citations
More filters
Journal ArticleDOI
A cross-modal genetic framework for the development and plasticity of sensory pathways
Laura Frangeul,Gabrielle Pouchelon,Ludovic Telley,Sandrine Lefort,Christian Lüscher,Christian Lüscher,Denis Jabaudon,Denis Jabaudon +7 more
TL;DR: It is reported that, despite their association with distinct sensory modalities, first-order nuclei in mice are genetically homologous across somatosensory, visual, and auditory pathways, as are higher order nuclei.
Journal ArticleDOI
Interval regression analysis using support vector networks
TL;DR: The v-support vector interval regression network (v-SVIRN) is proposed to evaluate interval linear and nonlinear regression models for crisp input and output data and is a model-free method in the sense that it does not have to assume the underlying model function.
Journal ArticleDOI
Improving biometric recognition accuracy and robustness using DWT and SVM watermarking
TL;DR: This paper presents a novel biometric watermarking algorithm for improving the recognition accuracy and protecting the face and fingerprint images from tampering using Multi-resolution Discrete Wavelet Transform and an intelligent learning algorithm based on υ-Support Vector Machine.
Journal ArticleDOI
Bus Arrival Time Prediction using Support Vector Machine with Genetic Algorithm
TL;DR: The experimental results show that the forecasting model is superior to the traditional SVM model and the Artificial Neural Network model in terms of the same data, and is of higher accuracy, which verified the feasibility of the model to predict the bus arrival time.
Book
An introduction to kernel methods
TL;DR: This Chapter describes how to use kernel methods for classification, regression and novelty detection and in each case it is found that training can be reduced to optimization of a convex cost function.
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.
Book
Matrix Analysis
Roger A. Horn,Charles R. Johnson +1 more
TL;DR: In this article, the authors present results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrate their importance in a variety of applications, such as linear algebra and matrix theory.
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.