Name
Abstract | ||
|---|---|---|
This paper presents a platform to bridge datamining techniques and concepts in the field of neurosciences with state-of-the-art data mining, in particular domain adaptation. In non-clinical environs, once an exhaustive search for a particular item of knowledge seems to be impractical, there is the natural tendency to switch to heuristic methods to expedite the search. Conversely, when neuroscientists are in the same situation, they will trust exhaustive searches rather than heuristics such as clinical decision-support systems (CDSS). This is particularly when electroencephalography (EEG) sequences are used to search for pathologic oscillations in the brain. The purpose of this paper is to promising results illustrating how an intelligent agent can data mine explicit types of pathologic oscillations in the human brain. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-41218-9_40 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Heuristic,Intelligent agent,Brute-force search,Domain adaptation,Computer science,Pathologic,Heuristics,Artificial intelligence,Clinical decision support system,Machine learning,Electroencephalography | Conference | 8170 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.40 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rory A. Lewis | 1 | 57 | 10.07 |
| Chad A. Mello | 2 | 4 | 2.20 |
| James Ellenberger | 3 | 1 | 0.40 |
| Andrew M. White | 4 | 154 | 11.33 |