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Section: Publications & Resources
The Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT) is hosting the Paths Ahead in the Science of Information and Decision Systems symposium on 12-14 Novemeber 2009 in Cambridge, MA. The symposium brings together leading researchers from all around the world who have been influential in shaping the vision of and leading the field of information and decision systems.
The Paths Ahead symposium will take a fresh look into old and new research
Section: Publications & Resources
Lecture Notes on multiview point cloud regularization (MVPCR), a framework that unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces. Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL.
Section: Publications & Resources
by Roger Woods, John McAllister, Ying Yi, and Gaye Lightbody.
Published by Wiley.
Section: Publications & Resources
John M. Cioffi and H. Vincent Poor have been elected International Fellows of the Royal Academy of Engineering of the United Kingdom, two of only three engineers worldwide to be so elected this year. They were inducted into the Academy at its New Fellows Dinner on 9 November 2009, in London, UK.
John Cioffi is Chairman and CEO of ASSIA, Inc., in Redwood City, CA, and Hitachi America Professor of Engineering, Emeritus, at Stanford University, where he received his PhD in 1984. Also an IEEE Fellow and a member of the U. S.
Section: Publications & Resources
The ability to learn about a stochastic process from noisy observations is fundamental to many applications. In order to track a dynamic process, typical knowledge representation is the state space model such as a linear Gauss Markov model, where efficient algorithms exist to perform state estimation under many different model assumptions. However, for meta level tracking, we are not only interested in the state estimation, but also temporal and structural classification of the process.
Section: Publications & Resources
Polarimetric Synthetic Aperture Radar (POLSAR) data is an important source for many operational remote sensing applications. Segmentation and classification of image data are important tasks for POLSAR data analysis and interpretation, which often requires human interaction. In this thesis, spectral graph partitioning methodology is used to exploit both the polarimetric attributes of pixels, and the visual aspect of the image data through visual cues.
