| 摘要 |
<p>The present invention relates to a method and a computer program for data compression and/or machine learning, such as data prediction including meta-learning of how to learn in a closed system with many levels of meta learning or infinite meta-levels. Compression is used both for the learning of target data and for meta-learning. A history entity is used for history compression to remember a trace of what the learning entity has done so far. The learning entity is provided with feed-back by adding random strings to the history entity. Random strings are a negative reinforcement for an entity which is trying to achieve compression. The reinforcement can be used both as an off-line system without an environment (internal reinforcement) and for external reinforcement from an environment.</p> |
