Name
Title | ||
|---|---|---|
Neural network adaptation process effectiveness dependent of constant training data availability |
Abstract | ||
|---|---|---|
The paper discusses and compares two different ways of adapting artificial intelligence systems. One is founded on a well known biological mechanism of gradual training of neurons or other parameters. The second one uses a significant extra feature of training data that ably makes us possible to adapt the artificial intelligence system in more effective way than nature does in biological systems. This extra feature is availability of all training data before the adaptation process begins till an end of which all these data have to be constant. This feature provides an ability to analyze training data globally and very quickly tune an artificial intelligence system with them. The paper focus the attention on this important difference between biological and artificial intelligence problems because in most cases of artificial intelligence problems training data are gathered, available and constant during the training process. On the other hand, the biological nervous systems gather training data during the whole life, have to change the inner model, so training is a very good solution for them because it makes them possible to tune with changing training data. Artificial intelligence systems can also use training inherent in biological systems but in most cases it is possible to find more quickly and effectively the solution if only the mentioned feature is met. The above thesis is illustrated by means of some examples. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.neucom.2009.03.017 | Neurocomputing |
Keywords | Field | DocType |
biological mechanism,gradual training,training data,neural network adaptation process,artificial intelligence problems training,artificial intelligence system,biological nervous system,biological system,constant training data availability,artificial intelligence problem,training process,extra feature,artificial intelligent,biological systems,data analysis,neural network,nervous system | Training set,Marketing and artificial intelligence,Computational intelligence,Computer science,Network construction,Artificial intelligence,Artificial neural network,Artificial Intelligence System,Machine learning | Journal |
Volume | Issue | ISSN |
72 | 13-15 | Neurocomputing |
Citations | PageRank | References |
8 | 0.83 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ewa Dudek-Dyduch | 1 | 66 | 10.30 |
| Ryszard Tadeusiewicz | 2 | 956 | 141.52 |
| Adrian Horzyk | 3 | 53 | 12.76 |