These results are of importance for practitioners, who may choose values for the smoothing constants arbitrarily, or by searching on the unit cube for values which minimize the sum of the squared errors when fitting the model to a data set. It is also shown that the variance of the forecast...
It submits all inputs to the network and computes the corresponding network outputs and errors, computing the sum of squared errors for all inputs (2) It first initializes the sensitivity, recursively calculates the sensitivity, and then augments each individual matrix into the Marquardt sensitivi...
The recent development of logical quantum processors marks a pivotal transition from the noisy intermediate-scale quantum (NISQ) era to the fault-tolerant quantum computing (FTQC) era. These devices have the potential to address classically challenging p
The huge growth and use of the Internet of Things (IoT) in everyday activities in combination with the limited resources present in the involved devices ha
Additionally, the MOT-SF method integrates a sum of product fusion strategies, combining segmented images from various scales to produce a final image that preserves both small and large PCOS structures while mitigating noise. The experimental results show that MOT-SF outperforms traditional methods ...
Most corporations and organizations rely heavily on access control to protect data accessibility and enable resource sharing across networks and departments. However, with the development of cloud computing, traditional boundary protection struggles to m
Its main idea is to choose the optimal K according to the varying trend of the sum of squared errors (SSE) in a cluster. SSE is defined as equation (10). (10) Here, Ci is the ith cluster, p is the sample vector of Ci, and mi is the center of cluster Ci.When K is smaller...
the loss function is now the sum of two terms: Note: there is a Baysian approach to deriving this. see https://jaan.io/what-is-variational-autoencoder-vae-tutorial for an excellent discussion. One of the interesting properties of VAEs is that they do not require massive data sets to co...
[1]. However, the current reliance on manual assessment presents inherent limitations, including the propensity for errors owing to tediousness and fatigue [2]. The need for quick identification of diverse prohibited items, including firearms and liquids beyond pre-set limits, adds to the ...
The goal of this paper is to compare several widely used Bayesian model selection methods in practical model selection problems, highlight their difference