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SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
13 years 6 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
ICRA
2009
IEEE
125views Robotics» more  ICRA 2009»
15 years 10 months ago
A novel method for learning policies from constrained motion
— Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently...
Matthew Howard, Stefan Klanke, Michael Gienger, Ch...
ICPR
2008
IEEE
15 years 10 months ago
Learning combined similarity measures from user data for image retrieval
Image retrieval has become an interesting and active field due to the increasing necessity of searching and browsing very large image repositories. Images are represented using s...
Miguel Arevalillo-Herráez, Francesc J. Ferr...
AIME
1997
Springer
15 years 8 months ago
Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods
We used Machine Learning (ML) methods to learn the best decision rules to distinguish normal brain aging from the earliest stages of dementia using subsamples of 198 normal and 244...
William Rodman Shankle, Subramani Mani, Michael J....
WAPCV
2007
Springer
15 years 10 months ago
Learning to Attend - From Bottom-Up to Top-Down
The control of overt visual attention relies on an interplay of bottom-up and top-down mechanisms. Purely bottom-up models may provide a reasonable account of the looking behaviors...
Hector Jasso, Jochen Triesch