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KDD
2009
ACM
198views Data Mining» more  KDD 2009»
15 years 10 months ago
Heterogeneous source consensus learning via decision propagation and negotiation
Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to conso...
Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han
ECML
2007
Springer
15 years 4 months ago
Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search
We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm i...
Daan Fierens, Jan Ramon, Maurice Bruynooghe, Hendr...
ICANN
2001
Springer
15 years 2 months ago
Learning and Prediction of the Nonlinear Dynamics of Biological Neurons with Support Vector Machines
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian kernels to learn and predict the nonlinear dynamics of single biological neurons. We...
Thomas Frontzek, Thomas Navin Lal, Rolf Eckmiller
ICIP
1999
IEEE
15 years 11 months ago
Integrating Stereo and Shape from Shading
This paper presents a new method for integrating di erent low level vision modules, stereo and shape from shading, in order to improve the 3D reconstruction of visible surfaces of...
Mostafa G.-H. Mostafa, Sameh M. Yamany, Aly A. Far...
ICML
2006
IEEE
15 years 10 months ago
Nightmare at test time: robust learning by feature deletion
When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier....
Amir Globerson, Sam T. Roweis