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ICML
2006
IEEE
16 years 8 months ago
A duality view of spectral methods for dimensionality reduction
We present a unified duality view of several recently emerged spectral methods for nonlinear dimensionality reduction, including Isomap, locally linear embedding, Laplacian eigenm...
Lin Xiao, Jun Sun 0003, Stephen P. Boyd
ICML
2005
IEEE
16 years 8 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore
ICML
2005
IEEE
16 years 8 months ago
Learning as search optimization: approximate large margin methods for structured prediction
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., li...
Daniel Marcu, Hal Daumé III
ISBI
2004
IEEE
16 years 8 months ago
Nonlinear Dimension Reduction of fMRI Data: The Laplacian Embedding Approach
In this paper, we introduce the use of nonlinear dimension reduction for the analysis of functional neuroimaging datasets. Using a Laplacian Embedding approach, we show the power ...
Olivier D. Faugeras, Bertrand Thirion
VLSID
2002
IEEE
130views VLSI» more  VLSID 2002»
16 years 7 months ago
Using Randomized Rounding to Satisfy Timing Constraints of Real-Time Preemptive Tasks
In preemptive real-time systems, a tighter estimate of the Worst Case Response Time(WCRT) of the tasks can be obtained if the layout of the tasks in memory is included in the esti...
Anupam Datta, Sidharth Choudhury, Anupam Basu