Name
Abstract | ||
|---|---|---|
In this paper a novel classification method for real world classification tasks is proposed. The method was designed to overcome the difficulties encountered by traditional methods when coping with those real world problems where the key issue is the detection of particular situations - such as for instance machine faults or anomalies - which in some frameworks are hard to be recognized due to some interacting factors that are analyzed within the paper. The method is described and tested on two industrial problems, which show the goodness of the proposed approach and encourage its use in the industrial environments. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02478-8_165 | IWANN (1) |
Keywords | Field | DocType |
traditional method,sensitive industrial classification tasks,real world classification task,instance machine fault,industrial problem,interacting factor,novel classification method,real world problem,key issue,thresholded neural networks,industrial environment,neural network | Computer science,Artificial intelligence,Artificial neural network,Machine learning | Conference |
Volume | ISSN | Citations |
5517 | 0302-9743 | 6 |
PageRank | References | Authors |
0.61 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Vannucci | 1 | 94 | 15.60 |
| Valentina Colla | 2 | 159 | 29.50 |
| Mirko Sgarbi | 3 | 26 | 5.72 |
| Orlando Toscanelli | 4 | 6 | 0.61 |