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Dr. Gong Tieliang of Xi'an Jiaotong University Was Invited to Give Lectures in Our Faculty
Author: Release time:2018-04-30 Number of clicks:

On the morning of April 27th, Dr. Gong Tieliang from Xi'an Jiaotong University was invited to give lectures in our faculty, and in the 201 Report Hall, the academic report entitled "Margin based PU Learning" was presented to the teachers and students of the faculty. And the report was presided over by Professor Zou Bin of the faculty. Teachers, Ph.D., and master graduate students from applied statistics, applied mathematics, and computational mathematics and other related majors participated in this academic report.

In the report, Dr. Gong Tieliang first used the examples from the ecology and classical support vector machines to elicit the PU Learning in the report, and then told us about the advantages and significance of PU Learning. After that he introduced the theoretical knowledge of support vector machine, optimal hyperplane, maximum classification interval and so on in detail, and proposed some directions that can be optimized in these knowledge theories. In the end, Dr. Gong Tieliang showed the optimal effect of PU Learning, he showed us more intuitive advantages of the algorithm with some of the experimental results he had done.

After the report, Dr. Gong Tieliang had more in-depth communication with the teachers and students present on the detailed questions in the report, and he also answered carefully the problems encountered by the students in their daily study.

After the report, Dr. Liantiliang a number of details of the report, and the presence of teachers and students in a more in-depth communication, and the students at ordinary times to learn some problems have been carefully answered.

Brief introduction of the lecturer:Gong Tieliang, a Ph.D. of Xi'an Jiaotong University. Research interests mainly focus on statistical learning theory and machine learning. More specifically, he specializes in generalized performance analysis of regularized learning algorithms, kernel methods, domain adaptation, and developing effective machine learning algorithms to solve practical problems.



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