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
An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss function
Umesh Gupta,Deepak Gupta +1 more
TL;DR: A new approach as improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss function (LAsy-ν-TSVR) which is more effective and efficient to deal with the outliers and noise.
Journal ArticleDOI
MHADBOR: AI-Enabled Administrative-Distance-Based Opportunistic Load Balancing Scheme for an Agriculture Internet of Things Network
TL;DR: In this article , a supervised machine learning multipath and administrative-distance-based load balancing algorithm for an AG-IoT network is proposed, which processes the collected information from source to destination by means of multihop count and administrative distance-based communication infrastructure in the network.
Journal ArticleDOI
Fault diagnosis of chemical processes considering fault frequency via Bayesian network
Book ChapterDOI
Multiple Instance Learning Allows MHC Class II Epitope Predictions Across Alleles
Nico Pfeifer,Oliver Kohlbacher +1 more
TL;DR: This work shows how to transform the problem of MHC class II binding peptide prediction into a well-studied machine learning problem called multiple instance learning, and introduces a new method for training a classifier of an allele without the necessity for binding allele data of the target allele.
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.