Name
Abstract | ||
|---|---|---|
This paper points out that many machine learning problems in IR should be and can be formalized in a novel way, referred to as 'group-based learning'. In group-based learning, it is assumed that training data as well as testing data consist of groups. The classifier is created and utilized across groups. Furthermore, evaluation in testing and also in training are conducted at group level, with the use of evaluation measures defined on a group. This paper addresses the problem and presents a Boosting algorithm to perform the new learning task. The algorithm, referred to as AdaBoost.Group, is proved to be able to improve accuracies in terms of group-based measures during training. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1458082.1458324 | CIKM |
Keywords | Field | DocType |
training data,group-based learning,group-based measure,boosting algorithm,group level,new learning task,paper point,boosting,data consistency,machine learning | Online machine learning,Instance-based learning,Multi-task learning,Stability (learning theory),Semi-supervised learning,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Machine learning,Learning classifier system | Conference |
Citations | PageRank | References |
4 | 0.44 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weijian Ni | 1 | 14 | 8.09 |
| Jun Xu | 2 | 1435 | 74.49 |
| Hang Li | 3 | 6294 | 317.05 |
| Yalou Huang | 4 | 744 | 53.86 |