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» Learning Models for Predicting Recognition Performance
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AAAI
2008
15 years 4 months ago
Hidden Dynamic Probabilistic Models for Labeling Sequence Data
We propose a new discriminative framework, namely Hidden Dynamic Conditional Random Fields (HDCRFs), for building probabilistic models which can capture both internal and external...
Xiaofeng Yu, Wai Lam
ICMCS
2000
IEEE
138views Multimedia» more  ICMCS 2000»
15 years 7 months ago
Event-Coupled Hidden Markov Models
Inferences from time-series data can be greatly enhanced by taking into account multiple modalities. In some cases, such as audio of speech and the corresponding video of lip gest...
Trausti T. Kristjansson, Brendan J. Frey, Thomas S...
ICML
2006
IEEE
16 years 3 months ago
Bayesian regression with input noise for high dimensional data
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
Jo-Anne Ting, Aaron D'Souza, Stefan Schaal
112
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SIGIR
2002
ACM
15 years 2 months ago
Probabilistic combination of text classifiers using reliability indicators: models and results
The intuition that different text classifiers behave in qualitatively different ways has long motivated attempts to build a better metaclassifier via some combination of classifie...
Paul N. Bennett, Susan T. Dumais, Eric Horvitz
123
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ICRA
2009
IEEE
208views Robotics» more  ICRA 2009»
15 years 7 days ago
Affordance based word-to-meaning association
Abstract-- This paper presents a method to associate meanings to words in manipulation tasks. We base our model on an affordance network, i.e., a mapping between robot actions, rob...
Verica Krunic, Giampiero Salvi, Alexandre Bernardi...