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» An Application of Boosting to Graph Classification
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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
SAC
2008
ACM
13 years 7 months ago
Towards a model-driven engineering approach for developing embedded hard real-time software
Model-Driven Engineering (MDE) has been advocated as an effective way to deal with today's software complexity. MDE can be seen as an integrative approach combining existing ...
Fabiano Cruz, Raimundo S. Barreto, Lucas Cordeiro
IJSI
2008
156views more  IJSI 2008»
13 years 7 months ago
Co-Training by Committee: A Generalized Framework for Semi-Supervised Learning with Committees
Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...
Mohamed Farouk Abdel Hady, Friedhelm Schwenker
CHI
2007
ACM
14 years 8 months ago
An initial investigation into non-visual computer supported collaboration
In this paper we present an initial study of computer supported collaboration between visually impaired users based around the interactive browsing and manipulation of simple grap...
David K. McGookin, Stephen A. Brewster
NIPS
2004
13 years 9 months ago
Triangle Fixing Algorithms for the Metric Nearness Problem
Various problems in machine learning, databases, and statistics involve pairwise distances among a set of objects. It is often desirable for these distances to satisfy the propert...
Inderjit S. Dhillon, Suvrit Sra, Joel A. Tropp