Peer-Reviewed Journals:
1) K. G. Bemis, D. Silver, G. Xu, R. Light, D. Jackson, C. Jones, S. Ozer, L. Lui, The Path to COVIS: a review of acoustic imaging of hydrothermal flow regimes " Journal of Deep-Sea Research Part II, Vol. 121, pp.159,176 ,2015.
2) S. Ozer, D. Silver, K. Bemis, P. Martin, " Activity Detection in Scientific Visualization ", Visualization and Computer Graphics, IEEE Transactions on, vol.20, no.3, pp.377,390, March 2014, doi: 10.1109/TVCG.2013.117. [PDF]
3) S. Ozer, C.H. Chen, H.A. Cirpan, "A Set of New Chebyshev Kernel Functions for Support Vector Machine Pattern Classification", Journal of Pattern Recognition, 44 (7), 1435-1447, 2011. [PDF]
4) S. Ozer, D. L. Langer, X. Liu, M. A. Haider, T. H. van der Kwast, A. J. Evans, Y. Yang, M. N. Wernick, I. S. Yetik, "Supervised and Unsupervised Methods for Prostate Cancer Segmentation With Multispectral MRI ", Journal of Medical Physics, Vol. 37, Issue:4, April 2010. [PDF]
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Peer-Reviewed Conferences, Workshops, Symposia:
13) S. Ozer, "Visual Object Recognition with Image Retrieval", Handbook of Pattern Recognition & Computer Vision, World Scientific, (invited book chapter), 2015.
12) R. Sarkar, S. Ozer, S. Acton, K. Skadron, "Image classification by multi-kernel dictionary learning”", IEEE Asilomar Conference on Signals, Systems and Computers, 2014.
11) S. Ozer, D. Silver, K. Bemis, P. Martin, "Activity Detection in Scientific Visualization (Invited Talk)" IEEE Visualization, 2014.
10) L. Liu, S. Ozer, K. Bemis, J. Takle, D. Silver, "An interactive method for activity detection visualization", Large Data Analysis and Visualization (LDAV), 2013 IEEE Symposium on, 2013 (Poster). [PDF]
9) K.G. Bemis, S. Ozer, G. Xu, P.A. Rona, D. Silver, "Event Detection for Hydrothermal Plumes: A case study at Grotto Vent", AGU Fall Meeting Abstracts 1, 05, 2012.
8) S. Ozer, J. Wei, D. Silver, K.-L. Ma, P. Martin, "Group Dynamics in Scientific Visualization", Large Data Analysis and Visualization (LDAV), 2012 IEEE Symposium on, 2012. [PDF]
7) S. Ozer, D. Silver, K. Bemis, P. Martin, J. Takle, "Activity Detection for Scientific Visualization", Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on, 117-118, 2011 (Poster). [PDF]
6) S. Ozer, D. Silver, P. Martin, "Group Tracking in Scientific Visualization", IEEE Visualization, Visweek, Providence, RI, 2011 (poster). [PDF]
5) S. Ozer, M.A. Haider, D.L. Langer, T.H. van der Kwast, A.J. Evans, M.N. Wernick, J. Trachtenberg, I.S. Yetik, "Prostate Cancer Localization with Multispectral MRI Based on Relevance Vector Machine", IEEE International Symposium on Biomedical Imaging, ISBI09, Boston, MA, June28-July01, 2009. [PDF]
4) S. Ozer, C.H. Chen, I.S. Yetik, "Using K-NN SVMs for Performance Improvement and Comparison to K-highest Lagrange Multipliers Selection", joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition, SSPR 2010, Springer- Verlag, Lecture Notes in Computer Science, August 2010. [PDF]
3) S. Ozer, C. H. Chen, "Generalized Chebyshev Kernels for Support Vector Classification", ICPR 08, International Conference on Pattern Recognition, 7-11 December 2008, Tampa-FL, USA. [PDF]
2) S. Ozer, H. A. Cirpan, N. Kabaoglu, "Support Vector Regression for Surveillance Purposes", MRCS - International Workshop on Multimedia Content Representation, Classification and Security, Springer- Verlag, Lecture Notes in Computer Science, Pages:442-449, 11-13 September 2006, Istanbul, Turkey.
1) S. Ozer, H. A. Cirpan, N. Kabaoglu, "Support Vector Machines Based Target Tracking Techniques" (in Turkish), IEEE SIU2006, Sinyal Isleme ve Uygulamalari Kurultayi, 17-19 April 2006, Antalya, Turkiye.
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Book: Sedat Ozer and Chi Hau Chen, "A kernel study on Support Vector Machines: Generalized Chebyshev Kernels", 2009. LAP Lambert Academic Publishing , ISBN-10: 3838305574 | ISBN-13: 978-3838305578.
About the book: Mainly, this is the published version of my M.Sc. thesis from University of Massachusetts, Dartmouth. The book may have some minor grammatical mistakes in it, since I had to check the grammar and edit it within a very limited time. Besides such mistakes, if you have any thoughts, questions or feedback about the book, I would be more than happy to hear them.
The book initially (and briefly) describes what machine learning is (specifically in the supervised-learning sense), and then goes into the theory of SVM, how the dual form of the SVM cost function is derived. Then it describes the previous work on Chebyshev kernels and then how to generalize them for vector inputs. Finally, it compares various kernel functions and their performances on various benchmark data sets including our proposed generalized Chebyshev kernel functions.
My "A Set of New Chebyshev Kernel Functions for Support Vector Machine Pattern Classification" journal paper reports most important parts of the book. Therefore I recommend reading the journal paper. (which can be obtained freely through many universities). |