学术报告报告题目一：Making Sense of Noisy Data: Some Issues and Methods
报 告 人：易耘 教授（滑铁卢大学）
报告时间：2018年9月18日（周二）下午14：50 -15：40
报告摘要：
Thanks to the advancement of modern technology in acquiring data, massive data with diverse features and big volume are becoming more accessible than ever. The impact of big data is significant. While the abundant volume of data presents great opportunities for researchers to extract useful information for new knowledge gain and sensible decision making, big data present great challenges. A very important, sometimes overlooked challenge is the quality and provenance of the data. Big data are not automatically useful; big data are often raw and involve considerable noise. Typically, the challenges presented by noisy data with measurement error, missing observations and high dimensionality are particularly intriguing. Noisy data with these features arise ubiquitously from various fields including health sciences, epidemiological studies, environmental studies, survey research, economics, and so on. In this talk, I will discuss the issues induced from noisy data and some methods of handling such data.
报告题目二：Data Adaptive Support Vector Machine with Application to Prostate Cancer Imaging Data
报 告 人：何文清 教授（西安大略大学）
报告时间：2018年9月18日（周二）下午15：40 -16：30
报告摘要：
Support vector machines (SVM) have been widely used as classifiers in various settings including pattern recognition, texture mining and image retrieval. However, such methods are faced with newly emerging challenges such as imbalanced observations and noise data. In this talk, I will discuss the impact of noise data and imbalanced observations on SVM classification and present a new data adaptive SVM classification method.
This work is motivated by a prostate cancer imaging study conducted in London Health Science Center. A primary objective of this study is to improve prostate cancer diagnosis and thereby to guide the treatment based on statistical predictive models. The prostate imaging data, however, are quite imbalanced in that the majority voxels are cancer-free while only a very small portion of voxels are cancerous. This issue makes the available SVM classifiers typically skew to one class and thus generate invalid results. Our proposed SVM method uses a data adaptive kernel to reflect the feature of imbalanced observations; the proposed method takes into consideration of the location of support vectors in the feature space and thereby generates more accurate classification results. The performance of the proposed method is compared with existing methods using numerical studies.
报告题目三：基于赋权图和模糊集的社交网络用户特征识别方法
报 告 人：魏福义教授（华南农业大学）
报告时间：2018年9月18日（周二）下午16：30 -17：20
报告人简介：
1986年本科毕业于兰州大学数学系；1995年研究生毕业于西安交通大学数学系。在华南农业大学任教，数学与信息学院副院长，教授。现任广东省运筹学会副理事长。主要从事组合数学、图论的研究。主持省部级项目9项，发表论文40余篇，SCI收录6篇，主编教材5部。
报告摘要：
社交网络中用户身份识别具有重要意义。如跨网络的用户身份识别可以对用户在其它平台进行兴趣推荐和识别社交网络中具有较大影响力的领袖用户等，对网络的监控和管理起到重要作用。本文将图论与模糊数学相结合，给出了网络用户识别的新方法。
报告地点：4号楼4318室
邀 请 人：杨启贵 教授
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数学学院
2018年9月17日