American Statistical Association
New York City
Metropolitan Area Chapter

New York State Psychiatric Institute
at Columbia University Medical Center
Biostatistics Seminar



Fernando Perez-Cruz, Ph.D.
Princeton University &
University Carlos III


Support vector machines were developed in the late nineteen-nineties for solving the general classification problem, in which the different nonparametric hypothesis are described by independent and identically distributed sampled data. Support vector machines are based on the statistical learning theory -Vapnik-Chervonenkis theory-, which describes the necessary and sufficient conditions for learning algorithms, i.e. the minimization of the empirical risk, to converge to the minimum risk and it gives performance bounds for finite sets. Support vector machines generalize the optimal hyperplane decision rule by nonlinearly transforming the data into a high-dimensional Hilbert space and describing its solution in terms of the kernel on that space.

Gaussian process classifiers are Bayesian nonparametric machine-learning tools for solving the same discriminative classification problem. They assume that a Gaussian process prior describes the latent classification function and the observations shape the prior to obtain a posterior probability estimate for each sample. Gaussian process classifiers, similarly to support vector machines, produce nonlinear decisions functions using nonlinear parametric covariance functions, whose parameters can be learnt by maximum likelihood or be marginalized out.

In this talk, we present both discriminative machine-learning procedures. We first describe support vector machines and how they implement the structural risk minimization principle. We introduce Gaussian process classifiers from its estimation counterpart and how they are able to produce accurate posterior probability estimates. We complete our presentation with some applications that have popularized these discriminative learning methods within the engineering and computer science communities.

Biographical Note

Fernando Perez-Cruz (IEEE Senior Member) was born in Sevilla, Spain, in 1973. He received a Ph.D. in Telecommunication Engineering in 2000 from the Technical University of Madrid and an MSc/BSc in Telecommunication Engineering from the University of Sevilla in 1996. He is an associate professor with the Department of Signal Theory and Communication at University Carlos III in Madrid and a visiting professor in Princeton University (Marie Curie Fellowship). He has held positions at the Gatsby Unit (London), Max Planck Institute for Biological Cybernetics (Tuebingen), BioWulf Technologies (New York) and Technical University of Madrid and Alcala University (Madrid). His current research interest lies in machine learning algorithmic and theoretical developments and information theory. He has authored over 60 contributions to international journals and peer-reviewed conferences.

Date: Tuesday, September 8, 2009
Time: 3:00 - 4:00 P.M.
Location: New York State Psychiatric Institute
1051 Riverside Drive
6th Floor Multipurpose Room (6602)
New York, New York


Coffee: 2:45 to 3:00 P.M.
Reception: 4:00 to 4:30 P.M.

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