Humans perform semi-supervised classification too

@article{citeulike:2581684, abstract = {We explore the connections between machine learning and human learning in one form of semi-supervised classification. 22 human subjects completed a novel 2-class categorization task in which they were first taught to categorize a single labeled example from each category, and subsequently were asked to categorize, without feedback, a large set of additional items. Stimuli were visually complex and unrecognizable shapes. The unlabeled examples were sampled from a bimodal distribution with modes appearing either to the left (leftshift condition) or right (right-shift condition) of the two labeled examples. Results showed that, although initial decision boundaries were near the middle of the two labeled examples, after exposure to the unlabeled examples, they shifted in different directions in the two groups. In this respect, the human behavior conformed well to the predictions of a Gaussian mixture model for semi-supervised learning. The human behavior differed from model predictions in other interesting respects, suggesting some fruitful avenues for future inquiry.}, author = {Zhu, X. and Rogers, T. and Qian, R. and Kalish, C. }, citeulike-article-id = {2581684}, journal = {AAAI Conference on Artificial Intelligence}, keywords = {computer\_science, feedback, integration, models, object\_recognition, semisupervised\_learning}, posted-at = {2008-03-24 18:08:55}, priority = {0}, title = {Humans perform semi-supervised classification too}, volume = {22}, year = {2007} }

TY - JOUR ID - citeulike:2581684 L3 - citeulike-article-id:2581684 TI - Humans perform semi-supervised classification too JF - AAAI Conference on Artificial Intelligence VL - 22 N2 - We explore the connections between machine learning and human learning in one form of semi-supervised classification. 22 human subjects completed a novel 2-class categorization task in which they were first taught to categorize a single labeled example from each category, and subsequently were asked to categorize, without feedback, a large set of additional items. Stimuli were visually complex and unrecognizable shapes. The unlabeled examples were sampled from a bimodal distribution with modes appearing either to the left (leftshift condition) or right (right-shift condition) of the two labeled examples. Results showed that, although initial decision boundaries were near the middle of the two labeled examples, after exposure to the unlabeled examples, they shifted in different directions in the two groups. In this respect, the human behavior conformed well to the predictions of a Gaussian mixture model for semi-supervised learning. The human behavior differed from model predictions in other interesting respects, suggesting some fruitful avenues for future inquiry. KW - computer_science KW - feedback KW - integration KW - models KW - object_recognition KW - semisupervised_learning AU - Zhu, X AU - Rogers, T AU - Qian, R AU - Kalish, C PY - 2007/// ER -