2010
NIPS
NeurIPS 2010
Avoiding False Positive in Multi-Instance Learning
Abstract
In multi-instance learning, there are two kinds of prediction failure, i.e., false negative and false positive. Current research mainly focus on avoding the former. We attempt to utilize the geometric distribution of instances inside positive bags to avoid both the former and the latter. Based on kernel principal component analysis, we define a projection constraint for each positive bag to classify its constituent instances far away from the separating hyperplane while place positive instances and negative instances at opposite sides. We apply the Constrained Concave-Convex Procedure to solve the resulted problem. Empirical results demonstrate that our approach offers improved generalization performance.
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Keyword Pioneer
— false positive
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
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Trend Setter
— Multi-Instance Learning
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Hot Topic Early Bird
— binary classification
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Learning Types > Weakly Supervised Learning
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Core Methods > Kernel Methods
Machine Learning > Learning Types > Classification
Machine Learning > Learning Types > Multi-Instance Learning