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ICML
2000
IEEE
16 years 3 months ago
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
Most machine learning algorithms are lazy: they extract from the training set the minimum information needed to predict its labels. Unfortunately, this often leads to models that ...
Joseph O'Sullivan, John Langford, Rich Caruana, Av...
CVPR
2006
IEEE
16 years 4 months ago
Meta-Evaluation of Image Segmentation Using Machine Learning
Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation algorithm, or parameterization of a given algorithm, is sele...
Hui Zhang, Sharath R. Cholleti, Sally A. Goldman, ...
118
Voted
ICANNGA
2007
Springer
149views Algorithms» more  ICANNGA 2007»
15 years 8 months ago
Using Real-Valued Meta Classifiers to Integrate and Contextualize Binding Site Predictions
Currently the best algorithms for transcription factor binding site predictions are severely limited in accuracy. However, a non-linear combination of these algorithms could improv...
Mark Robinson, Offer Sharabi, Yi Sun, Rod Adams, R...
138
Voted
CSFW
1999
IEEE
15 years 6 months ago
A Meta-Notation for Protocol Analysis
Most formal approaches to security protocol analysis are based on a set of assumptions commonly referred to as the "Dolev-Yao model." In this paper, we use a multiset re...
Iliano Cervesato, Nancy A. Durgin, Patrick Lincoln...
133
Voted
LICS
1994
IEEE
15 years 6 months ago
A Multiple-Conclusion Meta-Logic
The theory of cut-free sequent proofs has been used to motivate and justify the design of a number of logic programming languages. Two such languages, Prolog and its linear logic ...
Dale Miller