75 search results - page 5 / 15 » Building Text Classifiers Using Positive and Unlabeled Examp... |
ICCV
2003
IEEE
14 years 9 months ago
2003
IEEE
This paper discusses building complex classifiers from a single labeled example and vast number of unlabeled observation sets, each derived from observation of a single process or...
EMNLP
2010
13 years 5 months ago
2010
This paper studies the effects of training data on binary text classification and postulates that negative training data is not needed and may even be harmful for the task. Tradit...
AAAI
2004
13 years 8 months ago
2004
Traditionally, text classifiers are built from labeled training examples. Labeling is usually done manually by human experts (or the users), which is a labor intensive and time co...
FLAIRS
2007
13 years 9 months ago
2007
Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome t...
ICCV
2005
IEEE
14 years 9 months ago
2005
IEEE
Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). ...