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ICML
2010
IEEE
15 years 6 months ago
Gaussian Processes Multiple Instance Learning
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
Minyoung Kim, Fernando De la Torre
128
Voted
TEC
2008
104views more  TEC 2008»
15 years 4 months ago
Coevolution of Fitness Predictors
Abstract--We present an algorithm that coevolves fitness predictors, optimized for the solution population, which reduce fitness evaluation cost and frequency, while maintaining ev...
Michael D. Schmidt, Hod Lipson
IGARSS
2009
15 years 2 months ago
Unmixing Sparse Hyperspectral Mixtures
Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or ...
Marian-Daniel Iordache, José M. Bioucas-Dia...
VLSID
2008
IEEE
83views VLSI» more  VLSID 2008»
16 years 5 months ago
Efficient Linear Macromodeling via Discrete-Time Time-Domain Vector Fitting
We present a discrete-time time-domain vector fitting algorithm, called TD-VFz, for rational function macromodeling of port-to-port responses with discrete time-sampled data. The ...
Chi-Un Lei, Ngai Wong
ALT
2006
Springer
16 years 1 months ago
Teaching Memoryless Randomized Learners Without Feedback
The present paper mainly studies the expected teaching time of memoryless randomized learners without feedback. First, a characterization of optimal randomized learners is provided...
Frank J. Balbach, Thomas Zeugmann