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» Classification Using Multiple and Negative Target Rules
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SGAI
2007
Springer
14 years 1 months ago
Extending Jess to Handle Uncertainty
Computer scientists are often faced with the challenge of having to model the world and its associated uncertainties. One area in particular where modelling uncertainty is importa...
David Corsar, Derek H. Sleeman, Anne McKenzie
BMCBI
2006
158views more  BMCBI 2006»
13 years 7 months ago
Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
Background: Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the p...
Chaoyang Zhang, Peng Li, Arun Rajendran, Youping D...
ECCV
2010
Springer
13 years 7 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
WINE
2010
Springer
148views Economy» more  WINE 2010»
13 years 5 months ago
False-Name-Proofness in Social Networks
In mechanism design, the goal is to create rules for making a decision based on the preferences of multiple parties (agents), while taking into account that agents may behave stra...
Vincent Conitzer, Nicole Immorlica, Joshua Letchfo...
AUSAI
2007
Springer
14 years 1 months ago
Applying MCRDR to a Multidisciplinary Domain
This paper details updated results concerning an implementation of a Multiple Classification Ripple Down Rules (MCRDR) system which can be used to provide quality Decision Support ...
Ivan Bindoff, Byeong Ho Kang, Tristan Ling, Peter ...