Name
Abstract | ||
|---|---|---|
Information planning enables faster learning with fewer training examples. It is particularly applicable when training examples are costly to obtain. This work examines the advantages of information planning for text data by focusing on three supervised models: Naive Bayes, supervised LDA and deep neural networks. We show that planning based on entropy and mutual information outperforms random selection baseline and therefore accelerates learning. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Machine Learning | Naive Bayes classifier,Artificial intelligence,Mutual information,Sampling (statistics),Machine learning,Mathematics,Deep neural networks |
DocType | Volume | Citations |
Journal | abs/1802.03360 | 0 |
PageRank | References | Authors |
0.34 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vadim Smolyakov | 1 | 1 | 0.70 |
| John W. Fisher III | 2 | 878 | 74.44 |