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» Learning Object Representations Using Sequential Patterns
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IDA
2005
Springer
14 years 1 months ago
Removing Statistical Biases in Unsupervised Sequence Learning
Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize the...
Yoav Horman, Gal A. Kaminka
IVC
2000
104views more  IVC 2000»
13 years 7 months ago
Learning spatio-temporal patterns for predicting object behaviour
Rule-based systems employed to model complex object behaviours, do not necessarily provide a realistic portrayal of true behaviour. To capture the real characteristics in a specif...
Neil Sumpter, Andrew J. Bulpitt
AIIDE
2008
13 years 9 months ago
TAP: An Effective Personality Representation for Inter-Agent Adaptation in Games
Tactical Agent Personality (TAP) is a modeling concept to capture tactical patterns in game agents, based on a personality concept introduced by Tan and Cheng (2007), to allow beh...
Chek Tien Tan, Ho-Lun Cheng
ICCV
2007
IEEE
14 years 9 months ago
Discriminative Subsequence Mining for Action Classification
Recent approaches to action classification in videos have used sparse spatio-temporal words encoding local appearance around interesting movements. Most of these approaches use a ...
Sebastian Nowozin, Gökhan H. Bakir, Koji Tsud...
ICALT
2006
IEEE
14 years 1 months ago
A Semi-Automatic Tool using Ontology to Extract Learning Objects
The approach presented in this paper is intended for the semi-automatic construction of a learning object repository from HTML pages. An extraction method consists of applying the...
Bich-Liên Doan, Yolaine Bourda, Vasile Dumit...