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» Results of the KDD'99 Classifier Learning
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CVPR
2003
IEEE
14 years 11 months ago
Object Class Recognition by Unsupervised Scale-Invariant Learning
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constel...
Robert Fergus, Pietro Perona, Andrew Zisserman
NC
1998
13 years 10 months ago
Tirano System with Learning Capacity for the Detection of Moving Targets
A system based on the charge-discharge characteristics of the neural synapses of the visual path, is shortly introduced. The proposed system uses the LSR (length/speed ratio) descr...
Miguel Angel Fernández, Antonio Ferná...
BMCBI
2008
136views more  BMCBI 2008»
13 years 9 months ago
A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model
Background: Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also r...
Richard Judson, Fathi Elloumi, R. Woodrow Setzer, ...
JMLR
2006
123views more  JMLR 2006»
13 years 9 months ago
Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies
In this paper, we propose a number of adaptive prototype learning (APL) algorithms. They employ the same algorithmic scheme to determine the number and location of prototypes, but...
Fu Chang, Chin-Chin Lin, Chi-Jen Lu
PRL
2006
129views more  PRL 2006»
13 years 9 months ago
Learning spatial relations in object recognition
This paper studies two types of spatial relationships that can be learned from training examples for object recognition. The first one employs deformable relationships between obj...
Thang V. Pham, Arnold W. M. Smeulders