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» Evaluating learning algorithms and classifiers
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EMNLP
2008
13 years 12 months ago
Learning with Probabilistic Features for Improved Pipeline Models
We present a novel learning framework for pipeline models aimed at improving the communication between consecutive stages in a pipeline. Our method exploits the confidence scores ...
Razvan C. Bunescu
JETAI
2010
56views more  JETAI 2010»
13 years 8 months ago
Warning: statistical benchmarking is addictive. Kicking the habit in machine learning
Algorithm performance evaluation is so entrenched in the Machine Learning community that one could call it an addiction. Like most addictions, it is harmful and very difficult to ...
Chris Drummond, Nathalie Japkowicz
IEAAIE
2010
Springer
13 years 8 months ago
The Combination of a Causal and Emotional Learning Mechanism for an Improved Cognitive Tutoring Agent
This paper describes a Conscious Tutoring System (CTS) capable of dynamic fine-tuned assistance to users. We put forth the combination of a Causal Learning and Emotional learning m...
Usef Faghihi, Philippe Fournier-Viger, Roger Nkamb...
JMLR
2010
101views more  JMLR 2010»
13 years 5 months ago
Exploiting Feature Covariance in High-Dimensional Online Learning
Some online algorithms for linear classification model the uncertainty in their weights over the course of learning. Modeling the full covariance structure of the weights can prov...
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer...
ICCBR
2009
Springer
14 years 5 months ago
Improving Reinforcement Learning by Using Case Based Heuristics
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and ...
Reinaldo A. C. Bianchi, Raquel Ros, Ramon Ló...