sangdv

TS. Đinh Viết Sang

Giảng viên
Name  Dinh Viet Sang, PhD
Office Room 602 – B1 Building
Email sangdv@soict.hust.edu.vn

 

 
Tel 84-4-38696121
Fax  
Teaching 1.       Scientific computing

2.       Data Mining

3.       Discrete Maths

Research areas Machine Learning, Pattern Recognition, Regression, Data Mining, Image processing.
Publications

 

1. Dvoenko S., Sang D. Cross-Validation of Parametric Acyclic Models of Interrelated Objects // Proc. XI International Conference «Pattern Recognition and Image Analysis». – 2013. – Vol. 1.

2. Dvoenko S.D., Sang D.V. Estimating Parameters of Acyclic Markov Models in Raster Texture Image Segmentation (in Russian) // Izvestiya TSU. Technical Sciences. 2013. Vol. 2. P. 86-95.

3. Dvoenko S.D., Sang D.V. Raster Texture Image Recognition based on Parametric Acyclic Markov Fields (in Russian) // Proc. XXII International conference in Computer Graphic and Vision «GraphiCon-2012». – Moscow: Maks Press, 2012. P. 139–143.

4. Dvoenko S.D., Sang D.V. Parametric Acyclic Markov Models in the problem of Interrelated Object Recognition (in Russian) // Proc. IX International Conference «Intellectualization of Information Processing». – Moscow: Torus Press, 2012. P. 18–21.

5. Dvoenko S.D., Sang D.V. Algorithms for Adjusting Parameters of Combination of Acyclic Adjacency Graphs in the Problem of Texture Image Recognition (in Russian) // Izvestiya TSU. Technical Sciences. 2012. Vol. 3. P. 253-262.

6. Dvoenko S.D., Sang D.V. Algorithms for Adjusting Parameters of a Tree-like Markov Random Field in the Problem of Raster Texture Image Recognition (in Russian) // Izvestiya TSU. Natural Sciences. 2012. Vol. 1. P. 98-109.

7. Dvoenko S.D., Savenkov D.S., Sang D.V. Acyclic Markov Models in Analysis of Interrelated Data Array (in Russian) // Izvestiya TSU. Natural Sciences. 2010. Vol. 2. P. 173-185.

8. Dvoenko S.D., Savenkov D.S., Sang D.V. Combination of Acyclic Adjacency Graphs in the Problem of Markov Random Field Recognition (in Russian) // Proc. XIV conference «Mathematical methods for Pattern Recognition». – Moscow: Maks Press, 2009. P. 441–444.

Books  
Master students  
PhD students N/A
Other informations