Name
Abstract | ||
|---|---|---|
A consistent density function estimator is presented. Whether an estimator is consistent or not is critical when the desired solution is not known. Without determining consistency it is not possible to know if the solution generated by an algorithm is close to the true solution or not. A combination of performance index, MLP architecture, training algorithm, and statistical learning theory concepts is used to produce consistent one dimensional density function estimations. The performance index and the MELP architecture are designed using information theoretical and algorithmic considerations, whereas the consistency of the solution is determined from the behavior exhibited by the estimator throughout the training process. The training algorithm is designed to highlight behavior that has been proven to exist in other learning problems with the help of statistical learning theory. The algorithm is tested with examples in order to determine the extent of its usefulness and to study its limitations. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IJCNN.2006.246817 | 2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10 |
Keywords | Field | DocType |
learning artificial intelligence,performance index,multilayer perceptron,statistical analysis | Statistical learning theory,Stability (learning theory),Performance index,Pattern recognition,Computer science,Wake-sleep algorithm,Artificial intelligence,Generalization error,Probability density function,Perceptron,Machine learning,Estimator | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pablo Zegers | 1 | 35 | 6.32 |
| Jose G. Johnson | 2 | 1 | 0.70 |