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ICDM
2008
IEEE
109views Data Mining» more  ICDM 2008»
16 years 19 days ago
Learning by Propagability
In this paper, we present a novel feature extraction framework, called learning by propagability. The whole learning process is driven by the philosophy that the data labels and o...
Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim, Loon...
ICML
2009
IEEE
16 years 7 months ago
Predictive representations for policy gradient in POMDPs
We consider the problem of estimating the policy gradient in Partially Observable Markov Decision Processes (POMDPs) with a special class of policies that are based on Predictive ...
Abdeslam Boularias, Brahim Chaib-draa
AI
2004
Springer
15 years 11 months ago
Intrinsic Representation: Bootstrapping Symbols from Experience
If we are to understand human-level intelligence, we need to understand how meanings can be learned without explicit instruction. I take a step toward that understanding by showing...
Stephen David Larson
CICLING
2007
Springer
16 years 10 days ago
Learning for Semantic Parsing
Semantic parsing is the task of mapping a natural language sentence into a complete, formal meaning representation. Over the past decade, we have developed a number of machine lear...
Raymond J. Mooney
ACL
2006
15 years 7 months ago
Using String-Kernels for Learning Semantic Parsers
We present a new approach for mapping natural language sentences to their formal meaning representations using stringkernel-based classifiers. Our system learns these classifiers ...
Rohit J. Kate, Raymond J. Mooney