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NSF/DHS FODAVA Distinguished Lecture
By: Dr. Vladimir Vapnik
NEC Laboratories in Princeton, New Jersey, Columbia University, NY, NY and
Royal Holloway, University of London, England
Date: Friday, January 16, 2009
Time: 2:00pm-3:00pm
Location: Klaus 2447
For more information please contact Dr. Haesun Park; hpark@cc.gatech.edu
Title:
Learning with Teacher: Learning Using Hidden Information
Abstract:
The existing machine learning paradigm considers a simple
scheme: given a set of training examples find in a given collection of functions
the one that in the best possible way approximates the unknown
decision rule. In such a paradigm a teacher does not play an important role.
In human learning, however, the role of a teacher is very important:
along with examples a teacher provides students with explanations,
comments, comparisons, and so on.
In this talk I will introduce elements of human teaching in machine learning.
I will consider an advanced learning paradigm called learning using hidden information
(LUHI), where at the training stage a teacher gives some additional information $x^*$ about training example $x$. This information will not be available at the test stage.
I will consider the LUHI paradigm for support vector machine type of algorithms, demonstrate its superiority over the classical one and discuss general questions related to this paradigm.
Bio:
Vladimir Naumovich Vapnik is one of the main developers of Vapnik-Chervonenkis theory. He was born in the Soviet Union. He received his master's degree in mathematics at the Uzbek State University, Samarkand, Uzbek SSR in 1958 and Ph.D in statistics at the Institute of Control Sciences, Moscow in 1964. He worked at this institute from 1961 to 1990 and became Head of the Computer Science Research Department. At the end of 1990, he moved to the USA and joined the Adaptive Systems Research Department at AT&T Bell Labs in Holmdel, New Jersey. The group later became the Image Processing Research Department of AT&T Laboratories when AT&T spun off Lucent Technologies in 1996. Vapnik Left AT&T in 2002 and joined NEC Laboratories in Princeton, New Jersey, where he currently works in the Machine Learning group. He also holds a Professor of Computer Science and Statistics position at Royal Holloway, University of London since 1995, as well as an Adjunct Professor position at Columbia University, New York City since 2003. He was inducted into the U.S. National Academy of Engineering in 2006. While at AT&T, Vapnik and his colleagues developed the theory of the support vector machine. They demonstrated its performance on a number of problems of interest to the machine learning community, including handwriting recognition.
This event is sponsored by NSF/DHS FODAVA grant, School of Mathematics, Division of Computational Science and Engineering, Algorithms and Randomness Center, and Machine Learning and Data Mining Seminar Series grant by Yahoo.
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You are cordially invited to attend a reception that will follow the seminar to chat informally with faculty and students. Refreshments will be provided.
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Hope to see you there!
