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ICML
2006
IEEE
16 years 5 months ago
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is...
Alex Graves, Faustino J. Gomez, Jürgen Schmid...
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ICML
2006
IEEE
16 years 5 months ago
MISSL: multiple-instance semi-supervised learning
There has been much work on applying multiple-instance (MI) learning to contentbased image retrieval (CBIR) where the goal is to rank all images in a known repository using a smal...
Rouhollah Rahmani, Sally A. Goldman
ICML
2006
IEEE
16 years 5 months ago
Maximum margin planning
Mobile robots often rely upon systems that render sensor data and perceptual features into costs that can be used in a planner. The behavior that a designer wishes the planner to ...
Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinke...
ICML
2006
IEEE
16 years 5 months ago
Experience-efficient learning in associative bandit problems
We formalize the associative bandit problem framework introduced by Kaelbling as a learning-theory problem. The learning environment is modeled as a k-armed bandit where arm payof...
Alexander L. Strehl, Chris Mesterharm, Michael L. ...
ICML
2006
IEEE
16 years 5 months ago
Learning user preferences for sets of objects
Most work on preference learning has focused on pairwise preferences or rankings over individual items. In this paper, we present a method for learning preferences over sets of it...
Marie desJardins, Eric Eaton, Kiri Wagstaff