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92
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ICIP
2005
IEEE
16 years 4 months ago
Variable module graphs: a framework for inference and learning in modular vision systems
We present a novel and intuitive framework for building modular vision systems for complex tasks such as surveillance applications. Inspired by graphical models, especially factor...
Amit Sethi, Mandar Rahurkar, Thomas S. Huang
126
Voted
ILP
2003
Springer
15 years 8 months ago
Graph Kernels and Gaussian Processes for Relational Reinforcement Learning
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
Thomas Gärtner, Kurt Driessens, Jan Ramon
111
Voted
FLAIRS
2001
15 years 4 months ago
Graph-Based Concept Learning
We introduce the graph-based relational concept learner SubdueCL. We start with a brief description of other graph-based learning systems: the Galois lattice, Conceptual Graphs, a...
Jesus A. Gonzalez, Lawrence B. Holder, Diane J. Co...
136
Voted
SIGIR
2008
ACM
15 years 2 months ago
Learning query intent from regularized click graphs
This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a ...
Xiao Li, Ye-Yi Wang, Alex Acero
119
Voted
IJAR
2006
89views more  IJAR 2006»
15 years 2 months ago
Learning probabilistic decision graphs
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Manfred Jaeger, Jens D. Nielsen, Tomi Silander