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ICRA
2005
IEEE
150views Robotics» more  ICRA 2005»
15 years 3 months ago
Learning Sensor Network Topology through Monte Carlo Expectation Maximization
— We consider the problem of inferring sensor positions and a topological (i.e. qualitative) map of an environment given a set of cameras with non-overlapping fields of view. In...
Dimitri Marinakis, Gregory Dudek, David J. Fleet
ISMIR
2005
Springer
205views Music» more  ISMIR 2005»
15 years 3 months ago
Learning Harmonic Relationships in Digital Audio with Dirichlet-Based Hidden Markov Models
Harmonic analysis is a standard musicological tool for understanding many pieces of Western classical music and making comparisons among them. Traditionally, this analysis is done...
J. Ashley Burgoyne, Lawrence K. Saul
UAI
2001
14 years 11 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
AAAI
1998
14 years 11 months ago
Boosting in the Limit: Maximizing the Margin of Learned Ensembles
The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has sh...
Adam J. Grove, Dale Schuurmans
NAACL
1994
14 years 11 months ago
Learning from Relevant Documents in Large Scale Routing Retrieval
The normal practice of selecting relevant documents for training routing queries is to either use all relevants or the 'best n' of them after a (retrieval) ranking opera...
K. L. Kwok, Laszlo Grunfeld