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» Learning to Select Useful Landmarks
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ICML
2002
IEEE
14 years 10 months ago
Is Combining Classifiers Better than Selecting the Best One
We empirically evaluate several state-of-theart methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to se...
Saso Dzeroski, Bernard Zenko
RAS
2010
117views more  RAS 2010»
13 years 8 months ago
Extending BDI plan selection to incorporate learning from experience
An important drawback to the popular Belief, Desire, and Intentions (BDI) paradigm is that such systems include no element of learning from experience. We describe a novel BDI exe...
Dhirendra Singh, Sebastian Sardiña, Lin Pad...
ACCV
2010
Springer
13 years 4 months ago
Unsupervised Selective Transfer Learning for Object Recognition
Abstract. We propose a novel unsupervised transfer learning framework that utilises unlabelled auxiliary data to quantify and select the most relevant transferrable knowledge for r...
Wei-Shi Zheng, Shaogang Gong, Tao Xiang
TNN
2008
119views more  TNN 2008»
13 years 9 months ago
Selecting Useful Groups of Features in a Connectionist Framework
Abstract--Suppose for a given classification or function approximation (FA) problem data are collected using sensors. From the output of the th sensor, features are extracted, ther...
Debrup Chakraborty, Nikhil R. Pal
WAIM
2004
Springer
14 years 3 months ago
Learning-Based Top-N Selection Query Evaluation over Relational Databases
A top-N selection query against a relation is to find the N tuples that satisfy the query condition the best but not necessarily completely. In this paper, we propose a new method ...
Liang Zhu, Weiyi Meng