Support vector machines for interval discriminant analysis

Authors:
C. Angulo;D. Anguita;L. Gonzalez-Abril;J. A. Ortega
Affiliations:
GREC-Knowledge Engineering Research Group, Technical University of Catalonia, 08800 Vilanova i la Geltrú, Spain;DIBE-Department of Biophysical and Electronic Engineering, University of Genoa, 16145 Genoa, Italy;COSDE-Department of Applied Economics I, University of Seville, 41018 Seville, Spain;Computer Engineering Department, University of Seville, 41018 Seville, Spain
Venue:
Neurocomputing
Year:
2008

Citing 12
Cited 3

An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
AI Game Programming Wisdom

AI Game Programming Wisdom
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data

Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
An Interval Set Classification Based on Support Vector Machines

ICAS-ICNS '05 Proceedings of the Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services
Editorial

Computational Statistics
Dual unification of bi-class support vector machine formulations

Pattern Recognition
Kernel-based Algorithms and Visualization for Interval Data Mining

ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Efficient 16-bit Floating-Point Interval Processor for Embedded Systems and Applications

SCAN '06 Proceedings of the 12th GAMM - IMACS International Symposium on Scientific Computing, Computer Arithmetic and Validated Numerics
Support vector interval regression machine for crisp input and output data

Fuzzy Sets and Systems
Interval regression analysis by quadratic programming approach

IEEE Transactions on Fuzzy Systems
Interval regression analysis using quadratic loss support vector machine

IEEE Transactions on Fuzzy Systems
A Note on the Bias in SVMs for Multiclassification

IEEE Transactions on Neural Networks

Ameva: An autonomous discretization algorithm

Expert Systems with Applications: An International Journal
A new approach to qualitative learning in time series

Expert Systems with Applications: An International Journal
Integrating discretization and association rule-based classification for Alzheimer's disease diagnosis

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.02

Visualization

Abstract

The use of data represented by intervals can be caused by imprecision in the input information, incompleteness in patterns, discretization procedures, prior knowledge insertion or speed-up learning. All the existing support vector machine (SVM) approaches working on interval data use local kernels based on a certain distance between intervals, either by combining the interval distance with a kernel or by explicitly defining an interval kernel. This article introduces a new procedure for the linearly separable case, derived from convex optimization theory, inserting information directly into the standard SVM in the form of intervals, without taking any particular distance into consideration.