Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Nonconvex Online Support Vector Machines
Date
2011-02-01
Author
Ertekin Bolelli, Şeyda
Giles, C. Lee
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
57
views
0
downloads
Cite This
In this paper, we propose a nonconvex online Support Vector Machine (SVM) algorithm (LASVM-NC) based on the Ramp Loss, which has the strong ability of suppressing the influence of outliers. Then, again in the online learning setting, we propose an outlier filtering mechanism (LASVM-I) based on approximating nonconvex behavior in convex optimization. These two algorithms are built upon another novel SVM algorithm (LASVM-G) that is capable of generating accurate intermediate models in its iterative steps by leveraging the duality gap. We present experimental results that demonstrate the merit of our frameworks in achieving significant robustness to outliers in noisy data classification where mislabeled training instances are in abundance. Experimental evaluation shows that the proposed approaches yield a more scalable online SVM algorithm with sparser models and less computational running time, both in the training and recognition phases, without sacrificing generalization performance. We also point out the relation between nonconvex optimization and min-margin active learning.
Subject Keywords
Computational Theory and Mathematics
,
Software
,
Applied Mathematics
,
Artificial Intelligence
,
Computer Vision and Pattern Recognition
URI
https://hdl.handle.net/11511/41217
Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
DOI
https://doi.org/10.1109/tpami.2010.109
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Imbalance Problems in Object Detection: A Review.
Oksuz, Kemal; Cam, Baris Can; Kalkan, Sinan; Akbaş, Emre (Institute of Electrical and Electronics Engineers (IEEE), 2020-03-19)
In this paper, we present a comprehensive review of the imbalance problems in object detection. To analyze the problems in a systematic manner, we introduce a problem-based taxonomy. Following this taxonomy, we discuss each problem in depth and present a unifying yet critical perspective on the solutions in the literature. In addition, we identify major open issues regarding the existing imbalance problems as well as imbalance problems that have not been discussed before. Moreover, in order to keep our revi...
Dynamic programming for a Markov-switching jump-diffusion
Azevedo, N.; Pinheiro, D.; Weber, Gerhard Wilhelm (Elsevier BV, 2014-09-01)
We consider an optimal control problem with a deterministic finite horizon and state variable dynamics given by a Markov-switching jump-diffusion stochastic differential equation. Our main results extend the dynamic programming technique to this larger family of stochastic optimal control problems. More specifically, we provide a detailed proof of Bellman's optimality principle (or dynamic programming principle) and obtain the corresponding Hamilton-Jacobi-Belman equation, which turns out to be a partial in...
Order of convergence of evolution operator method
Ergenc, T; Hascelik, AI (Informa UK Limited, 1998-01-01)
In this paper the order of convergence of the evolution operator method used to solve a nonlinear autonomous system in ODE's [2] is investigated. The order is found, to be 2N+1 where N comes from the [N+1,N] Pade' approximation used in the method. The order is independent of the choice of the weight function.
A statistical approach to sparse multi-scale phase-based stereo
Ulusoy, İlkay (Elsevier BV, 2007-09-01)
In this study, a multi-scale phase based sparse disparity algorithm and a probabilistic model for matching uncertain phase are proposed. The features used are oriented edges extracted using steerable filters. Feature correspondences are estimated using phase-similarity at multiple scale using a magnitude weighting scheme. In order to achieve sub-pixel accuracy in disparity, we use a fine tuning procedure which employs the phase difference between corresponding feature points. We also derive a probabilistic ...
A nested iterative scheme for computation of incompressible flows in long domains
Manguoğlu, Murat; Tezduyar, Tayfun E.; Sathe, Sunil (Springer Science and Business Media LLC, 2008-12-01)
We present an effective preconditioning technique for solving the nonsymmetric linear systems encountered in computation of incompressible flows in long domains. The application category we focus on is arterial fluid mechanics. These linear systems are solved using a nested iterative scheme with an outer Richardson scheme and an inner iteration that is handled via a Krylov subspace method. Test computations that demonstrate the robustness of our nested scheme are presented.
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
Ş. Ertekin Bolelli and C. L. Giles, “Nonconvex Online Support Vector Machines,”
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, pp. 368–381, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41217.