(Frichot et al., 2014, Frichot and François, 2015) with the following parameters: five α (regularization parameter) values (10, 50, 100, 500, 1000), K values of 1 to 12 (K = 1:12), and 100 runs per K value, and the minimum cross entropy as TRUE to estimate the best number...
0 is a concentration parameter, and the conditional probability distribution above, wmek denotes the can see that number of a customer customers seated at the kth table. From is more likely to sit at a table if there are already many people sitting there. However, a customer will ...
penalties is controlled by a user-specified mixing parameter, while the overall penalty controlled by a tuning parameter to be determined in a data-dependent way, such as through cross-validation. This general approach has been applied to linear models, generalized linear models (GLMs) [11], and...
in which η(arg; ε) is a continuous regularization function evaluated at scalar argument arg and parameter ε. In order for Equation 8 to closely reproduce 7, η was chosen as follows: (9)η(arg;ε)=1+tanh(arg/e)2={1,arg>+ξ0,arg<−ξ in which 0 < ξ << 1. ξ is a fu...
This approach does not require an a priori chosen regularization parameter and can be viewed as a combination of training and model selection (see below). 1.1.2 Reinforcement learning Evolutionary algorithms are reinforcement learning (RL) methods in the definition of Sut- ton and Barto (1998). ...
TL can be roughly classified into four categories: instance-based TL [24], parameter-based TL [25], relation-based TL [26], and feature-based TL [27, 28]. Among them, the feature-based TL is the most investigated method because of the ability of correcting cross-domain discrepancy. The...
The objective function of the NCFS consists of a parameterλ, which is the ratio of the mean loss of the NCA regression model to the regularization term. The parameter is determined by processes such as thek-fold cross validation and the grid search algorithm, which are computationally expensive...
where 𝜙𝑗ϕj represents a predefined Gaussian kernel function; 𝛼𝑗αj is the parameter corresponding to the basis function. Because different basis function 𝜙𝑗ϕj settings will affect the result of the density ratio estimation, sample 𝑐𝑗cj is selected from the target domain ...
To mitigate overfitting, L2 regularization is applied to restrict the training process of the model using the specified parameter value of 5 × 10−4. It worth mentioning that we chose an empirical value of this parameter. In practice, it could be optimized by experiments. Algorithm 1 Mini-...
where 𝜙𝑗ϕj represents a predefined Gaussian kernel function; 𝛼𝑗αj is the parameter corresponding to the basis function. Because different basis function 𝜙𝑗ϕj settings will affect the result of the density ratio estimation, sample 𝑐𝑗cj is selected from the target domain ...