The phrase "automatic estimation" is correct and usable in written English.
It can be used in contexts where a process or system is designed to calculate or assess something without manual intervention.
Example: "The software features automatic estimation of project costs based on historical data and current parameters."
Alternatives: "automated assessment" or "self-calculating estimate".
Principal features of the tool are: automatic generation of generalized mass action model from the chemical reactions involved in the system; automatic estimation of the initial guesses and bounds for the parameter values; estimate of the propagation of the experimental errors from the input data to the parameter estimates; estimation of the level of noise in the input data.
Further efforts are still needed: we consider that a CNN can be used to estimate QF1, and our future work will focus on the automatic estimation of QF1.
"Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions". 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017): 2318-2327.
... We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites.
Here, we present a method for the automatic estimation of pain intensity from skin conductance data, and test it in a dataset containing physiological responses to nociceptive heat pain.
Although the top method within each clustering subcategory estimate the number of modules implicitly (coinciding with a relatively low parameter sensitivity) (Fig. 2b), we found that implicit or automatic estimation of the number of modules is not a prerequisite for a high performance (Supplementary Fig. 9).
Our method is based on the semi-automatic estimation of optimal threshold values.
To ensure such convergence, parameters were estimated using a novel automatic parameter estimation technique.
Mean errors in automatic volume estimation ranged from 0.07 to 0.6%, depending upon capillary diameter.
In this paper we present A-BIRCH, an approach for automatic threshold estimation for the BIRCH clustering algorithm.
Automatic strain estimation associated with normal faults is achieved for synthetic and real fault scarps on Mars.
