286 search results - page 11 / 58 » Learning to Classify Texts Using Positive and Unlabeled Data |
KDD
2007
ACM
14 years 9 months ago
2007
ACM
Many important application areas of text classifiers demand high precision and it is common to compare prospective solutions to the performance of Naive Bayes. This baseline is us...
ALT
1998
Springer
14 years 1 months ago
1998
Springer
Learning from positive examples occurs very frequently in natural learning. The PAC learning model of Valiant takes many features of natural learning into account, but in most case...
AUSAI
2008
Springer
13 years 10 months ago
2008
Springer
Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...
SDM
2010
SIAM
13 years 10 months ago
2010
SIAM
Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications, especially for Internet classification tasks like review spam...
PR
2007
13 years 8 months ago
2007
In relevance feedback algorithms, selective sampling is often used to reduce the cost of labeling and explore the unlabeled data. In this paper, we proposed an active learning alg...