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North-Holland, Amsterdam THE IMMUNE SYSTEM, ADAPTATION, AND MACHINE LEARNING
| Content Provider | Semantic Scholar |
|---|---|
| Author | Packard, Norman H. Perelson, Alan S. |
| Copyright Year | 2004 |
| Abstract | The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://tuvalu.santafe.edu/~jdf/papers/immunesystem.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
