| 摘要 |
A sequence walk model associates connections with system states. The model is capable of modeling systems that have liner state sequences. Intuitively a system modeled by a sequence walk model is like an object moving around a set of locations. The connections the object uses determine which locations the object will move to. And the locations the object moves to determine the connections that can be used by the object. In the same way the states of a system in the past may determine the sates of a system in the future. The process of moving from location to location is known as a walk process and the mathematical properties of walk processes have been well developed over time. The properties of a walk process are parameters of a sequence walk model. The present invention is a machine learning system that utilizes sequence walk model technology. A sequence walk model is a framework or a model that is assigned parameters with the intention of obtaining an optimal functionality and hence becomes available to perform a wide range of varied functions which may be carried out by the ultimate end user of the sequence walk model. The system described in the present invention is capable of, among other things, predicting the behavior of a system, classifying an unlabeled system, operating as a system with custom functionality, being a system with functionality that imitates the functionality of another system and providing greater understanding and knowledge of real-world systems.
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