Sentence examples for minimized prediction error from inspiring English sources

We used the lars function in the lars package [ 21] of R and used cross validation on the training data to find the number of markers that minimized prediction error.

Thus, the matching of an automaton, or computer, with a real physical environment could result in the anticipation of the future, if certain mathematical equations were derived that minimized prediction error.

Here we investigate whether systems that minimize prediction error e.g. predictive coding, can also show creativity, or on the contrary, prediction error minimization unqualifies for the design of systems that respond in creative ways to non-recurrent problems.

The brain attempts to minimize prediction error (the component of the input that is not predicted by the brain's current model).

Free energy, as Friston defined it, was roughly equivalent to what Clark called prediction error; and the brain's need to minimize free energy, or minimize prediction error, Friston believed, drove everything the brain did.

When the brain strove to minimize prediction error, it was not just trying to reduce its uncertainty about what was going on in the world; it was struggling to resolve the contradictions between fantasy and reality ideally by making reality more like fantasy.

This is then aggregated with the first to minimize prediction error for the labeled instances in the target.

Furthermore, the account successfully overcomes the "dark-room" problem, explaining why we do not avoid stimulation to minimize prediction error.

Irrespective of the smearing factor used, its value in minimizing prediction error depends crucially on the presence and nature of heteroskedasticity in the log scale residuals.

By minimizing prediction error in interacting with the environment, the net is forced to develop the conceptual resources to model the causal structure of the external world, and so navigate that world more effectively.

The regularization employed by the GLM-path algorithm is designed to minimize prediction error over independent validation datasets [ 34].