By Thomas G. Dietterich (auth.)
This ebook constitutes the refereed lawsuits of the 1st overseas Workshop on a number of Classifier platforms, MCS 2000, held in Cagliari, Italy in June 2000.
The 33 revised complete papers awarded including 5 invited papers have been rigorously reviewed and chosen for inclusion within the booklet. The papers are prepared in topical sections on theoretical matters, a number of classifier fusion, bagging and boosting, layout of a number of classifier platforms, purposes of a number of classifier structures, record research, and miscellaneous purposes.
Read or Download Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings PDF
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Extra resources for Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings
Sample text
7 Conclusions It should be emphasized that our analysis is based on a single experiment for a single dataset. Conclusions will thereby at most point in a possible direction. They can be summarized as follows: • Combining classifiers trained on different feature sets is very useful, especially when in these feature set probabilities are well estimated by the classifier. Combining different classifiers trained on the same classifier on the other hand may also improve, but is generally far less useful.
Sharkey et al. g. [16] [12] [4]), although the focus here is on the test and select methodology. Data was obtained by physically introducing (at different times) four common faults in a 4-stroke twin cylinder diesel engine. The four faults were (i) leaking air inlet, (ii) leaking exhaust valve, (iii) partial blockage of one in four injector nozzle holes, and (iv) injector dribble or leak. Data was collected under each faulty condition, as well as during normal operation. Data corresponding to in cylinder pressure, and to engine vibration were collected; in-cylinder pressure by means of a pressure transducer sensor, and vibration by means of an accelerometer attached to the outside of the cylinder.
Each vector is presented to the previously trained SOM in turn, and the winning output node recorded. That output node is then associated (or labelled) with the corresponding reference location. Were it not for the problem of perceptual aliasing (see below), following training and labelling of the SOM, the input vector of a “lost robot” could be mapped by the SOM onto the appropriate output category, which in turn would identify its reference location. However, perceptual aliasing complicates the process.