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AAAI
2008
15 years 7 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
TMM
2002
104views more  TMM 2002»
15 years 4 months ago
Spatial contextual classification and prediction models for mining geospatial data
Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for i...
Shashi Shekhar, Paul R. Schrater, Ranga Raju Vatsa...
ICASSP
2011
IEEE
14 years 8 months ago
Evaluating music sequence models through missing data
Building models of the structure in musical signals raises the question of how to evaluate and compare different modeling approaches. One possibility is to use the model to impute...
Thierry Bertin-Mahieux, Graham Grindlay, Ron J. We...
AIED
2011
Springer
14 years 8 months ago
Causal Modeling of User Data from a Math Learning Environment with Game-Like Elements
We have created a math learning environment with game-like elements such as narrative, visual feedback, personalization, collection, etc. We made a study with four different versio...
Dovan Rai, Joseph E. Beck
MICCAI
2007
Springer
15 years 11 months ago
Robust Autonomous Model Learning from 2D and 3D Data Sets
In this paper we propose a weakly supervised learning algorithm for appearance models based on the minimum description length (MDL) principle. From a set of training images or volu...
Georg Langs, Rene Donner, Philipp Peloschek, Horst...