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NIPS
2007
15 years 5 months ago
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...
Ruslan Salakhutdinov, Geoffrey E. Hinton
ICML
2004
IEEE
16 years 4 months ago
Lookahead-based algorithms for anytime induction of decision trees
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
Saher Esmeir, Shaul Markovitch
DATE
2003
IEEE
82views Hardware» more  DATE 2003»
15 years 9 months ago
A Circuit SAT Solver With Signal Correlation Guided Learning
— Boolean Satistifiability has attracted tremendous research effort in recent years, resulting in the developments of various efficient SAT solver packages. Based upon their de...
Feng Lu, Li-C. Wang, Kwang-Ting Cheng, Ric C.-Y. H...
UAI
2001
15 years 5 months ago
Improved learning of Bayesian networks
The search space of Bayesian Network structures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of Baye...
Tomás Kocka, Robert Castelo
PAMI
2008
196views more  PAMI 2008»
15 years 4 months ago
Distance Learning for Similarity Estimation
In this paper, we present a general guideline to find a better distance measure for similarity estimation based on statistical analysis of distribution models and distance function...
Jie Yu, Jaume Amores, Nicu Sebe, Petia Radeva, Qi ...