Committee-Based Sample Selection for Probabilistic Classifiers Export

@article{citeulike:1290770, abstract = {In many real-world learning tasks it is expensive to acquire a sufficient number of labeled examples for training. This paper investigates methods for reducing annotation cost by sample selection. In this approach, during training the learning program examines many unlabeled examples and selects for labeling only those that are most informative at each stage. This avoids redundantly labeling examples that contribute little new information. Our work follows on previous research on Query By...}, author = {Engelson, Shlomo A. and Dagan, Ido}, citeulike-article-id = {1290770}, citeulike-linkout-0 = {http://citeseer.ist.psu.edu/argamon-engelson99committeebased.html}, citeulike-linkout-1 = {http://citeseer.lcs.mit.edu/argamon-engelson99committeebased.html}, citeulike-linkout-2 = {http://citeseer.ifi.unizh.ch/argamon-engelson99committeebased.html}, citeulike-linkout-3 = {http://citeseer.comp.nus.edu.sg/argamon-engelson99committeebased.html}, journal = {Journal of Artificial Intelligence Research}, keywords = {active-learning, ie}, pages = {335--360}, posted-at = {2008-02-08 17:15:55}, priority = {2}, title = {Committee-Based Sample Selection for Probabilistic Classifiers}, url = {http://citeseer.ist.psu.edu/argamon-engelson99committeebased.html}, volume = {11}, year = {1999} }

TY - JOUR ID - citeulike:1290770 L3 - citeulike-article-id:1290770 N2 - In many real-world learning tasks it is expensive to acquire a sufficient number of labeled examples for training. This paper investigates methods for reducing annotation cost by sample selection. In this approach, during training the learning program examines many unlabeled examples and selects for labeling only those that are most informative at each stage. This avoids redundantly labeling examples that contribute little new information. Our work follows on previous research on Query By... JF - Journal of Artificial Intelligence Research EP - 360 TI - Committee-Based Sample Selection for Probabilistic Classifiers VL - 11 SP - 335 KW - active-learning KW - ie AU - Engelson, Shlomo AU - Dagan, Ido PY - 1999/// UR - http://citeseer.ist.psu.edu/argamon-engelson99committeebased.html ER -