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» Evaluating learning algorithms and classifiers
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CVPR
2007
IEEE
15 years 11 days ago
What makes a good model of natural images?
Many low-level vision algorithms assume a prior probability over images, and there has been great interest in trying to learn this prior from examples. Since images are very non G...
Yair Weiss, William T. Freeman
TSE
2008
148views more  TSE 2008»
13 years 10 months ago
Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings
Software defect prediction strives to improve software quality and testing efficiency by constructing predictive classification models from code attributes to enable a timely ident...
Stefan Lessmann, Bart Baesens, Christophe Mues, Sw...
ICANNGA
2009
Springer
212views Algorithms» more  ICANNGA 2009»
14 years 4 months ago
Evolutionary Regression Modeling with Active Learning: An Application to Rainfall Runoff Modeling
Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alte...
Ivo Couckuyt, Dirk Gorissen, Hamed Rouhani, Eric L...
ECAI
2000
Springer
14 years 2 months ago
Similarity-based Approach to Relevance Learning
In several information retrieval (IR) systems there is a possibility for user feedback. Many machine learning methods have been proposed that learn from the feedback information in...
Rickard Cöster, Lars Asker
UAI
2001
13 years 11 months ago
Aggregating Learned Probabilistic Beliefs
We consider the task of aggregating beliefs of several experts. We assume that these beliefs are represented as probability distributions. We argue that the evaluation of any aggr...
Pedrito Maynard-Reid II, Urszula Chajewska