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
Assessment of the usefulness of particle size distribution measured by laser diffraction for soil water retention modelling
Krzysztof Lamorski,Andrzej Bieganowski,Magdalena Ryżak,Agata Sochan,Cezary Sławiński,Wioleta Stelmach +5 more
TL;DR: In this article, four different PTF models based on LDM particle size distribution data were developed, with different PSD characteristics taken as the models' input variables, for PTF development.
Proceedings ArticleDOI
Extracting cues from speech for predicting severity of Parkinson'S disease
Meysam Asgari,Izhak Shafran +1 more
TL;DR: The results show that the information extracted from speech can predict the motor subscore of the clinical measure, the Unified Parkinson's Disease Rating Scale, within a mean absolute error of 5.7 and a standard deviation of about 2.0.
Journal ArticleDOI
Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression
Chien-Chun Yang,Mahesh B. Nagarajan,Markus B. Huber,Julio Carballido-Gamio,Jan S. Bauer,Thomas Baum,Felix Eckstein,Eva-Maria Lochmüller,Sharmila Majumdar,Thomas M. Link,Axel Wismüller,Axel Wismüller +11 more
TL;DR: The results suggest that supplementing standard DXA BMD measurements with sophisticated femoral trabecular bone characterization and supervised learning techniques can significantly improve biomechanical strength prediction in proximal femur specimens.
Journal ArticleDOI
Improving peptide identification in proteome analysis by a two-dimensional retention time filtering approach.
TL;DR: The combination of a two-dimensional peptide separation scheme based on reversed-phase and ion-pair reversed phase HPLC with a computational method to model and predict retention times in both dimensions facilitated an increase in true positive peptides upon lowering mass spectrometric scoring thresholds and concomitantly filtering out false positives on the basis of predicted retention times.
Proceedings ArticleDOI
Simpler knowledge-based support vector machines
TL;DR: A simple method is introduced to incorporate prior knowledge in support vector machines by modifying the hypothesis space rather than the optimization problem, which finds a local optimum.
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.