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Institute of Theoretical Physics |
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Chinese Academy of Sciences |
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State Key Laboratory of Theoretical Physics |
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Seminar |
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Title
题目 |
A Support Vector Machine (SVM) for Functional Data Analysis |
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Speaker
报告人 |
Dr. Hongli Zeng (曾红丽) |
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Affiliation
所在单位 |
Department of Mathematics, Uppsala University, Sweden. |
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Date
日期 |
7月15日，星期三，上午10:30--11:30, |
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Venue
地点 |
ITP New Building 6420, 理论物理研究所新楼四层多媒体教室 |
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Abstract
摘要 |
This talk will present the Longitudinal Support Vector Machine (LSVM) algorithm. This is an extension of the standard SVM towards classification of functional data. Functional data or longitudinal data represent observations which are made repeatedly over time. This problem requires modifications of the definition of the margin which underlies the standard SVM. The resulting LSVM results also in a convex optimisation problem, and the dual optimisation problem is derived.
We will present results on a specific application concerning the evolution of brain structures. Here we are interested in classifying the brain structures arising in evolution governed by natural selection, and in evolution governed by artificial selection. That is, we are interested in the differences of brain structures in either evolution regime. This talk then demonstrate how one can use the LSVM for analysis.
Empirical results indicate the efficacy of the LSVM for analysing such functional and evolutionary data. |
