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» Learning Models for Predicting Recognition Performance
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AUSAI
2008
Springer
14 years 11 months ago
Learning Object Representations Using Sequential Patterns
This paper explores the use of alternating sequential patterns of local features and saccading actions to learn robust and compact object representations. The temporal encoding rep...
Nobuyuki Morioka
CORR
2006
Springer
111views Education» more  CORR 2006»
14 years 9 months ago
An associative memory for the on-line recognition and prediction of temporal sequences
This paper presents the design of an associative memory with feedback that is capable of on-line temporal sequence learning. A framework for on-line sequence learning has been prop...
Joy Bose, Stephen B. Furber, Jonathan L. Shapiro
ICASSP
2008
IEEE
15 years 4 months ago
Discriminative learning for optimizing detection performance in spoken language recognition
We propose novel approaches for optimizing the detection performance in spoken language recognition. Two objective functions are designed to directly relate model parameters to tw...
Donglai Zhu, Haizhou Li, Bin Ma, Chin-Hui Lee
AIED
2007
Springer
15 years 3 months ago
Predicting Students' Performance with SimStudent: Learning Cognitive Skills from Observation
SimStudent is a machine-learning agent that learns cognitive skills by demonstration. SimStudent was originally built as a building block for Cognitive Tutor Authoring Tools to hel...
Noboru Matsuda, William W. Cohen, Jonathan Sewall,...
ICCV
2007
IEEE
15 years 11 months ago
Active Learning with Gaussian Processes for Object Categorization
Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gauss...
Ashish Kapoor, Kristen Grauman, Raquel Urtasun, Tr...