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AAAI
2007
13 years 9 months ago
Learning Large Scale Common Sense Models of Everyday Life
Recent work has shown promise in using large, publicly available, hand-contributed commonsense databases as joint models that can be used to infer human state from day-to-day sens...
William Pentney, Matthai Philipose, Jeff A. Bilmes...
JCP
2006
118views more  JCP 2006»
13 years 7 months ago
Learning a Classification-based Glioma Growth Model Using MRI Data
Gliomas are malignant brain tumors that grow by invading adjacent tissue. We propose and evaluate a 3D classification-based growth model, CDM, that predicts how a glioma will grow ...
Marianne Morris, Russell Greiner, Jörg Sander...
ICML
2004
IEEE
14 years 8 months ago
Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm
In kernel methods, an interesting recent development seeks to learn a good kernel from empirical data automatically. In this paper, by regarding the transductive learning of the k...
Zhihua Zhang, Dit-Yan Yeung, James T. Kwok
ICML
2010
IEEE
13 years 8 months ago
Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in d...
Tobias Lang, Marc Toussaint
KDD
1998
ACM
120views Data Mining» more  KDD 1998»
13 years 11 months ago
Large Datasets Lead to Overly Complex Models: An Explanation and a Solution
This paper explores unexpected results that lie at the intersection of two common themes in the KDD community: large datasets and the goal of building compact models. Experiments ...
Tim Oates, David Jensen