The phrase "generalization capacity" is correct and usable in written English.
It refers to the ability to make broad statements or draw conclusions based on specific examples or instances. You can use "generalization capacity" when discussing a person's critical thinking skills or their ability to analyze and understand complex information. For example: - "The student's generalization capacity was evident in their ability to apply concepts learned in class to real-world scenarios." - "The company's success can be attributed to its employees' strong generalization capacity, allowing them to adapt to changing market trends." - "The study found a correlation between high generalization capacity and creative problem-solving abilities."
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 results revealed that the proposed models have good prediction and generalization capacity with acceptable errors.
The pruning of the network is a process consisting of removing unnecessary parameters (weights) and nodes during the training process of the network without losing its generalization capacity.
Unlike the standard approaches to perception, we argue that this mapping draws on internal causal and compositional models of the physical world and these internal models underlie the generalization capacity of human perception.
According to the results, the PSO algorithm successfully improved the RBFN while the RBFN-PSO model's generalization capacity enhanced, with RMSE of 0.071 compared to the RBFN-BP model with RMSE of 0.114 in the testing dataset.
Each model of SNPSVM considers not only the compactness in both classes by the structural information but also the separability between classes, thus it can fully exploit prior knowledge to directly improve the algorithm׳s generalization capacity.
Alternatively, those comparison algorithms seem to show quite large level oscillations that indicate that the classifiers lack stability and good generalization capacities across different data sets, which probably exclude themselves as candidates for clinical proteomics diagnosis.
This is due to a small capacity of generalization for this big group of samples, which means that new samples adds little information.
The major limitation of this phenomenon is the lack of community orientation in a modern society, capacity, and generalization of a model elsewhere.
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.
