Paper Title:
Multiple-Instance Classification via Generalized Eigenvalue Proximal SVM
  Abstract

The multiple-instance classification problem is formulated using a linear or nonlinear kernel as the minimization of a linear function in a finite dimensional real space subject to linear and bilinear constraints by SVM-based methods. This paper presents a new multiple-instance classifier that determines two nonparallel planes by solving generalized eigenvalue proximal SVM. Our method converges in a few iterations to a local solution. Computational results on a number of datasets indicate that the proposed algorithm is competitive with the other SVM-based methods in multiple-instance classification.

  Info
Periodical
Advanced Materials Research (Volumes 143-144)
Edited by
H. Wang, B.J. Zhang, X.Z. Liu, D.Z. Luo, S.B. Zhong
Pages
1235-1239
DOI
10.4028/www.scientific.net/AMR.143-144.1235
Citation
Z. Wang, D. M. Li, "Multiple-Instance Classification via Generalized Eigenvalue Proximal SVM", Advanced Materials Research, Vols. 143-144, pp. 1235-1239, 2011
Online since
October 2010
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Hui Juan Xiong, B. Yu
Abstract:Min-max-min programming is an important but difficult nonsmooth programming. An aggregate homotopy method was given for solving min-max-min...
669
Authors: Dong Wang, Shi Huan Xiong
Chapter 8: Nanomaterials and Nanomanufacturing
Abstract:The learning sequence is an important factor of affecting the study effect about incremental Bayesian classifier. Reasonable learning...
1455
Authors: Ying Wang
Chapter 4: Image and Video Processing, Algorithms of Optimization
Abstract:This paper proposes a new image Multi-Instance (MI) bag generating method, which models an image with a Gaussian Mixed Model (GMM). The...
338
Authors: Tao Chen, Hui Fang Deng
Chapter 3: Signal and Data Processing, Data Mining, Applied and Computational Mathematics
Abstract:In this paper, we propose a novel method for image retrieval based on multi-instance learning with relevance feedback. The process of this...
1606