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ICMLA
2009
13 years 6 months ago
Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule
Languages that combine predicate logic with probabilities are needed to succinctly represent knowledge in many real-world domains. We consider a formalism based on universally qua...
Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapu...
ICCV
2011
IEEE
12 years 9 months ago
Annotator Rationales for Visual Recognition
Traditional supervised visual learning simply asks annotators “what” label an image should have. We propose an approach for image classification problems requiring subjective...
Jeff Donahue, Kristen Grauman
AIED
2005
Springer
14 years 2 months ago
Relation-based heuristic diffusion framework for LOM generation
Learning Object Metadata (LOM) intends to facilitate the retrieval and reuse of learning material. However, the fastidious task of authoring them limits their use. Motivated by thi...
Olivier Motelet
AAAI
2006
13 years 10 months ago
Preference Elicitation and Generalized Additive Utility
Any automated decision support software must tailor its actions or recommendations to the preferences of different users. Thus it requires some representation of user preferences ...
Darius Braziunas, Craig Boutilier
COR
2006
97views more  COR 2006»
13 years 9 months ago
Evaluating the performance of cost-based discretization versus entropy- and error-based discretization
Discretization is defined as the process that divides continuous numeric values into intervals of discrete categorical values. In this article, the concept of cost-based discretiz...
Davy Janssens, Tom Brijs, Koen Vanhoof, Geert Wets