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» A Framework for Multiple-Instance Learning
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MM
2004
ACM
124views Multimedia» more  MM 2004»
14 years 1 months ago
An online-optimized incremental learning framework for video semantic classification
This paper considers the problems of feature variation and concept uncertainty in typical learning-based video semantic classification schemes. We proposed a new online semantic c...
Jun Wu, Xian-Sheng Hua, HongJiang Zhang, Bo Zhang
HPDC
2009
IEEE
13 years 11 months ago
Maestro: a self-organizing peer-to-peer dataflow framework using reinforcement learning
In this paper we describe Maestro, a dataflow computation framework for Ibis, our Java-based grid middleware. The novelty of Maestro is that it is a self-organizing peer-to-peer s...
C. van Reeuwijk
ACL
2007
13 years 9 months ago
A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity
A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as...
Feiyu Xu, Hans Uszkoreit, Hong Li
ICDAR
2009
IEEE
14 years 2 months ago
A Probabilistic Framework for Soft Target Learning in Online Cursive Handwriting Recognition
To develop effective learning algorithms for online cursive word recognition is still a challenge research issue. In this paper, we propose a probabilistic framework to model the ...
Xiaoyuan Zhu, Yong Ge, Feng-Jun Guo, Li-Xin Zhen
ICRA
2007
IEEE
126views Robotics» more  ICRA 2007»
14 years 2 months ago
A formal framework for robot learning and control under model uncertainty
— While the Partially Observable Markov Decision Process (POMDP) provides a formal framework for the problem of robot control under uncertainty, it typically assumes a known and ...
Robin Jaulmes, Joelle Pineau, Doina Precup