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» Learning from Little: Comparison of Classifiers Given Little...
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MICCAI
2000
Springer
13 years 9 months ago
Small Sample Size Learning for Shape Analysis of Anatomical Structures
We present a novel approach to statistical shape analysis of anatomical structures based on small sample size learning techniques. The high complexity of shape models used in medic...
Polina Golland, W. Eric L. Grimson, Martha Elizabe...
CG
2000
Springer
13 years 10 months ago
Chess Neighborhoods, Function Combination, and Reinforcement Learning
Abstract. Over the years, various research projects have attempted to develop a chess program that learns to play well given little prior knowledge beyond the rules of the game. Ea...
Robert Levinson, Ryan Weber
BMCBI
2010
224views more  BMCBI 2010»
13 years 5 months ago
An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data
Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal ...
Susmita Datta, Vasyl Pihur, Somnath Datta
SBP
2012
Springer
12 years 1 months ago
Lessons Learned in Using Social Media for Disaster Relief - ASU Crisis Response Game
In disasters such as the earthquake in Haiti and the tsunami in Japan, people used social media to ask for help or report injuries. The popularity, efficiency, and ease of use of s...
Mohammad Ali Abbasi, Shamanth Kumar, Jose Augusto ...
AAAI
2010
13 years 7 months ago
Activity and Gait Recognition with Time-Delay Embeddings
Activity recognition based on data from mobile wearable devices is becoming an important application area for machine learning. We propose a novel approach based on a combination ...
Jordan Frank, Shie Mannor, Doina Precup