B. K. Ghosh. A brief history of sequential analysis. In B. K. Ghosh and P. K. Sen, editors, Handbook of Sequential Analysis, chapter 1, pages 1--19. Marcel Dekker, New York, 1991. Home/Search Document Not in Database Summary Related Articles Check |
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Heterogeneous Uncertainty Sampling for Supervised Learning - Lewis, Catlett (1994) (70 citations) (Correct)
....by t score. The null hypothesis was that differences in average error rate across the 10 runs for each category were normally distributed with mean zero and a category specific variance. We believe uncertainty sampling and other sequential, active, or exploratory approaches to learning [12, 25] enable both learning research and learning applications on large, complex, real world data sets where fixed training sets are impracticable. Natural language processing, where there is great interest in inducing knowledge to support tagging, parsing, semantic interpretation, and other forms of ....
B. K. Ghosh. A brief history of sequential analysis. In B. K. Ghosh and P. K. Sen, editors, Handbook of Sequential Analysis, chapter 1, pages 1--19. Marcel Dekker, New York, 1991.
An Integrated System for Filtering News and.. - Amati, D'Aloisi.. (Correct)
....can be assimilated to the uncertainty sampling [18, 19] Indeed, ProFile is endowed with the class of uncertain documents, in which the user may activate a new learning phase. In [18, 19] Lewis and Gale observed a better performance in using uncertainty sampling instead of relevance sampling [13] when the sample size is small in comparison with the number of positive examples in the set of non evaluated data. This is an important feature of ProFile, because the first set of evaluated document in the training set is very small (typically, a user wants to activate the filtering phase after ....
Ghosh, G., A brief history of sequential analysis, chapter 1, pages 1-19, Marcel Dekker, New York, 1991.
A Sequential Algorithm for Training Text Classifiers - Lewis, Gale (1994) (122 citations) (Correct)
....feedback has many problems as an approach to sampling. It works more poorly as the classifier improves, and is susceptible to selecting redundant examples. Relevance sampling is a sequential approach to sampling, since the labeling of earlier examples influences the selection of later ones [6]. This paper describes an alternative sequential approach, uncertainty sampling, motivated by results in computational learning theory. Uncertainty sampling is an iterative process of manual labeling of examples, classifier fitting from those examples, and use of the classifier to select new ....
B. K. Ghosh. A brief history of sequential analysis. In B. K. Ghosh and P. K. Sen, editors, Handbook of Sequential Analysis, chapter 1, pages 1--19. Marcel Dekker, New York, 1991.
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