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Extra info for 43.Pattern Analysis and Machine Intelligence

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In complex modeling tasks finding the underlying structural properties of the domain area is of utmost importance. It can be claimed that in the perceptron networks, for example, the ontological assumption fails: It is hypothesized that because these networks can implement any function, they can be used to model the mental functions as well, without the need of studying the properties of these functions beforehand. However, having no prior restrictions to the class of functions to be searched for, immense amounts of data are needed for training.

The robot systems are again very simple. But perhaps there is no need to go back to the simplest control systems — control theory has matured during the decades, too. In modern control theory one emphasizes the role of model-based control — what is this model like now? System theory. System theory is the theory about how to construct models. Additional challenges to traditional system theories are caused by the intuition that the mental model should not be centralized but distributed. It seems that the framework of dynamic systems offers tools also for understanding tensions caused by the environment: there is a dynamic equilibrium, determined by the attractor structures of the environmental properties.

It need not be known whether the variables represent actual input channels or connections to lower-level subsystems carrying out more elementary categorization tasks. The variables can also be outputs to some motoric unit. Assuming that at least three long-term memory units are available, the following three vectors corresponding to the above rules can be stored in LTM (empty slots denote “no connection”, represented by zeros in the mathematical formulation): 1≤i≤NSTM 2. The “loading” of the feature c is now φc = θcT W f.

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