Name
Abstract | ||
|---|---|---|
Virtually all existing multi-task learning methods for string data require either domain specific knowledge to extract feature representations or a careful setting of many input parameters. In this work, we propose a feature-free and parameter-light multi-task clustering algorithm for string data. To transfer knowledge between different domains, a novel dictionary-based compression dissimilarity measure is proposed. Experimental results with extensive comparisons demonstrate the generality and the effectiveness of our proposal. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21916-0_14 | ISMIS |
Field | DocType | Volume |
Data mining,Compression (physics),Pattern recognition,Kolmogorov complexity,Computer science,String Data.,Artificial intelligence,Cluster analysis,Generality,Machine learning | Conference | 6804 |
ISSN | Citations | PageRank |
0302-9743 | 3 | 0.39 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thach Huy Nguyen | 1 | 17 | 2.73 |
| Hao Shao | 2 | 19 | 3.36 |
| Bin Tong | 3 | 40 | 8.11 |
| Einoshin Suzuki | 4 | 853 | 93.41 |