Sentence examples for capacity of generalization from inspiring English sources

This makes S-TWSVM fully exploit these prior knowledge to directly improve the algorithm's the capacity of generalization.

This is due to a small capacity of generalization for this big group of samples, which means that new samples adds little information.

This is because this research intended to create a system with high capacity of generalization, and in this way, each individual is compared to all the individuals in the database.

Finally, the fact that familiarity did not modify within-class responses demonstrates a capacity of generalization, which is one of the cue properties of categorization.

Therefore, the capacity of generalization of our cerebellar neural network was tested in two ways: 1/In the first session, the performance in the other amplitudes than those used for training were assessed, i.e. interpolations to 15° and 25° and extrapolations to 5°, 35° and 40° for up and down directions.

This definition could have the predicate calculus representation: This representation shows that an intelligent agent who has no implicit knowledge of the hypothetical physical world and no capacity of generalization or analogy making, must be explicitly provided with all knowledge necessary to reason about "bricks" in that limited reality.

The study included a small sample size which further limits the capacity for generalization.

Simultaneously, the left or right vector acting on matrix pattern in the multi-class matrixized objective function plays a role of a tradeoff parameter to balance the capacity of learning and generalization.

The major limitation of this phenomenon is the lack of community orientation in a modern society, capacity, and generalization of a model elsewhere.

In mSVDD, we leverage the probabilistic framework with kernel-based method, where the model comprises two quantities: log likelihood to control the fit of data to the model (i.e., empirical error) and regularization quantizer to control the generalization capacity of the model (i.e., general error).

The two studies presented above raise the question about how to increase the generalization capacity of Deep Learning data representations and patterns, noting that the ability to generalize learnt patterns is an important requirement in Big Data Analytics where often there is a distribution shift between the input domain and the target domain.