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» Robust Boosting for Learning from Few Examples
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AAAI
2000
14 years 11 months ago
A Quantitative Study of Small Disjuncts
Systems that learn from examples often express the learned concept in the form of a disjunctive description. Disjuncts that correctly classify few training examples are known as s...
Gary M. Weiss, Haym Hirsh
ICCV
2009
IEEE
16 years 2 months ago
Which Faces to Tag: Adding Prior Constraints into Active Learning
We introduce an algorithm that guides the user to tag faces in the best possible order during a face recognition assisted tagging scenario. In particular, we extend the active l...
Ashish Kapoor, Gang Hua, Amir Akbarzadeh and Simon...
ECCV
2006
Springer
15 years 1 months ago
Comparing Ensembles of Learners: Detecting Prostate Cancer from High Resolution MRI
While learning ensembles have been widely used for various pattern recognition tasks, surprisingly, they have found limited application in problems related to medical image analysi...
Anant Madabhushi, Jianbo Shi, Michael D. Feldman, ...
ICASSP
2008
IEEE
15 years 4 months ago
Learning the kernel via convex optimization
The performance of a kernel-based learning algorithm depends very much on the choice of the kernel. Recently, much attention has been paid to the problem of learning the kernel it...
Seung-Jean Kim, Argyrios Zymnis, Alessandro Magnan...
NIPS
2007
14 years 11 months ago
Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition
We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs). In real-world applications suc...
Maryam Mahdaviani, Tanzeem Choudhury