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DIS
2006
Springer
15 years 9 months ago
Optimal Bayesian 2D-Discretization for Variable Ranking in Regression
In supervised machine learning, variable ranking aims at sorting the input variables according to their relevance w.r.t. an output variable. In this paper, we propose a new relevan...
Marc Boullé, Carine Hue
IJCAI
1997
15 years 7 months ago
Learning Topological Maps with Weak Local Odometric Information
cal maps provide a useful abstraction for robotic navigation and planning. Although stochastic mapscan theoreticallybe learned using the Baum-Welch algorithm,without strong prior ...
Hagit Shatkay, Leslie Pack Kaelbling
WSC
2008
15 years 8 months ago
Demand forecast of semiconductor products based on technology diffusion
Demand forecast plays a critical role to determine capital investment for capacity planning. Given the involved uncertainties and long lead-time for capacity expansion, semiconduc...
Chen-Fu Chien, Yun-Ju Chen, Jin-Tang Peng
CVPR
2009
IEEE
17 years 1 months ago
Co-training with Noisy Perceptual Observations
Many perception and multimedia indexing problems involve datasets that are naturally comprised of multiple streams or modalities for which supervised training data is only sparsely...
Ashish Kapoor, Chris Mario Christoudias, Raquel Ur...
ICML
2007
IEEE
16 years 7 months ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney