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Oral
Humans Learn Using Manifolds, Reluctantly
Bryan R Gibson · Jerry Zhu · Timothy T Rogers · Chuck Kalish · Joseph Harrison

Thu Dec 09 11:30 AM -- 11:50 AM (PST) @ Regency Ballroom
in Oral Session 18 »

When the distribution of unlabeled data in feature space lies along a manifold, the information it provides may be used by a learner to assist classification in a semi-supervised setting. While manifold learning is well-known in machine learning, the use of manifolds in human learning is largely unstudied. We perform a set of experiments which test a human's ability to use a manifold in a semi-supervised learning task, under varying conditions. We show that humans may be encouraged into using the manifold, overcoming the strong preference for a simple, axis-parallel linear boundary.

Author Information

Bryan R Gibson (University of Wisconsin-Madison)
Jerry Zhu (University of Wisconsin-Madison)
Timothy T Rogers (University of Wisconsin-Madison)
Chuck Kalish (University of Wisconsin-Madison)
Joseph Harrison

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