As systems with machine learning and data mining are getting more and more wide-spread, the need for integrated object oriented machine learning systems are increasing. Such system is required to integrate machine learning with the classical object oriented representation at least in three ways:
1. Implement machine learning on complex objects: Everyday problems usually can not be represented by simple atomic values, like real numbers, but rather on complex information objects on which some kind of an automated association must be carried out. In this sense important part of such systems would be to work somehow with structured data.
2. Some machine learning transformations of the system can be better represented as a kind of a data flow of objects. Like a simple machine learning algorithm usually has some kind of a pre- transformation phase, a real learning phase and some kind of a post transformation phase. These phases can be easily represented as a flow of transformation objects.
3. Complex systems are usually not based only on machine learning algorithms, but rather machine learning is a part of the whole systems. In this sense, it would be important to model the machine learning really as an integrated part of the system not something that is totally separated. Possibilities might be self adaptable types, properties, entities or even associations between different objects or types.
To sum it up, machine learning is usually imagined pretty much independent from the classical object oriented systems or highly structured knowledge representations systems. However, it is not necessarily the best approach. Instead integrated approaches should be available, to be able to easily design hybrid systems both having machine learning and strucured knowledge representation parts.
