Name
Abstract | ||
|---|---|---|
This paper introduces a novel classification algorithm for heterogeneous domain adaptation. The algorithm projects both the target and source data into a common feature space of the class decomposition scheme used. The distinctive features of the algorithm are: (1) it does not impose any assumptions on the data other than sharing the same class labels; (2) it allows adaptation of multiple source domains at once; and (3) it can help improving the topology of the projected data for class separability. The algorithm provides two built-in classification rules and allows applying any other classification model. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-93034-3_14 | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2018, PT I |
Field | DocType | Volume |
Data mining,Feature vector,Domain adaptation,Computer science,Source data,Class separability | Conference | 10937 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Firat Ismailoglu | 1 | 3 | 1.74 |
| Evgueni N. Smirnov | 2 | 24 | 20.38 |
| Ralf L. M. Peeters | 3 | 62 | 22.61 |
| Shuang Zhou | 4 | 10 | 5.02 |
| pieter collins | 5 | 29 | 5.17 |