New Support Vector Algorithms
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
Automatic prediction of intelligible speaking rate for individuals with ALS from speech acoustic and articulatory samples
Jun Wang,Prasanna V. Kothalkar,Myung Jong Kim,Andrea Bandini,Beiming Cao,Yana Yunusova,Thomas F. Campbell,Daragh Heitzman,Jordan R. Green +8 more
TL;DR: The proposed analyses predicted the intelligible speaking rate of the participant with reasonably high accuracy by extracting the acoustic and/or articulatory features from one short speech sample and may be well-suited for clinical applications that require automatic speech severity prediction.
Journal ArticleDOI
Support vector machines in DSC-based glioma imaging: suggestions for optimal characterization.
TL;DR: The combination of automated tumor segmentation followed by SVM classification is feasible and a powerful tool is available to characterize glioma presurgically in patients.
Proceedings Article
From Regression to Classification in Support Vector Machines
TL;DR: It is shown that for a given SVMC solution there exists a SVMR solution which is equivalent for a certain choice of the parameters, and SVMC can be seen as a special case of SVMR.
Journal ArticleDOI
Error tolerance based support vector machine for regression
TL;DR: An online error tolerance based support vector machine (ET-SVM) which not only grows but also prunes support vectors and can significantly reduce computational time while ensuring satisfactory learning accuracy is proposed.
Journal ArticleDOI
Hyper)Graph Embedding and Classification via Simplicial Complexes
TL;DR: This paper investigates a novel graph embedding procedure based on simplicial complexes and proposes two real-world applications, namely predicting proteins’ enzymatic function and solubility propensity starting from their 3D structure in order to give an example of the knowledge discovery phase which can be carried out starting from the proposed embedding strategy.
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.