2009
NIPS
NeurIPS 2009
Distribution Matching for Transduction
Abstract
Many transductive inference algorithms assume that distributions over training and test estimates should be related, e.g. by providing a large margin of separation on both sets. We use this idea to design a transduction algorithm which can be used without modification for classification, regression, and structured estimation. At its heart we exploit the fact that for a good learner the distributions over the outputs on training and test sets should match. This is a classical two-sample problem which can be solved efficiently in its most general form by using distance measures in Hilbert Space. It turns out that a number of existing heuristics can be viewed as special cases of our approach.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
📈
Trend Setter
— Transfer Learning
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Keyword Pioneer
— transductive inference
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Speech & Audio
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Hot Topic Early Bird
— semi-supervised learning
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Regression
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Learning Types > Transfer Learning
Machine Learning > Learning Paradigms > Semi-Supervised Learning