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» Learning to Identify Unexpected Instances in the Test Set
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TKDE
2010
182views more  TKDE 2010»
13 years 4 months ago
MILD: Multiple-Instance Learning via Disambiguation
In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set ...
Wu-Jun Li, Dit-Yan Yeung
TIFS
2010
135views more  TIFS 2010»
13 years 16 days ago
Distance Metric Learning for Content Identification
This paper considers a distance metric learning (DML) algorithm for a fingerprinting system, which identifies a query content by finding the fingerprint in the database (DB) that m...
Dalwon Jang, Chang Dong Yoo, Ton Kalker
ECCV
2008
Springer
13 years 7 months ago
Multiple Instance Boost Using Graph Embedding Based Decision Stump for Pedestrian Detection
Pedestrian detection in still image should handle the large appearance and stance variations arising from the articulated structure, various clothing of human as well as viewpoints...
Junbiao Pang, Qingming Huang, Shuqiang Jiang
KDD
2003
ACM
150views Data Mining» more  KDD 2003»
14 years 6 months ago
Learning relational probability trees
Classification trees are widely used in the machine learning and data mining communities for modeling propositional data. Recent work has extended this basic paradigm to probabili...
Jennifer Neville, David Jensen, Lisa Friedland, Mi...
CEC
2010
IEEE
13 years 7 months ago
Hyper-heuristics with a dynamic heuristic set for the home care scheduling problem
A hyper-heuristic performs search over a set of other search mechanisms. During the search, it does not require any problem-dependent data. This structure makes hyperheuristics pro...
Mustafa Misir, Katja Verbeeck, Patrick De Causmaec...