The phrase "a non regularized" is not correct in standard written English; it should be "a non-regularized." You can use it in contexts related to statistics, machine learning, or any field where regularization is discussed, referring to a model or method that does not apply regularization techniques.
Example: "The results from the non-regularized model showed a significant overfitting issue."
Alternatives: "an unregularized" or "a non-regularized model."
The torque at drill-bit was modeled considering a non-regularized dry friction model, with parameters that were adjusted using empirical data proposed in literature.
The approach is very general, which makes it possible to solve the hypersingular BIE numerically in a non-regularized form by using conforming C0 quadratic boundary elements and standard Gaussian quadratures without paying special attention to the corner treatment.
The efficiency of a regularized linear classifier (Fisher's Linear Discriminant Analysis, LDA) and a non-regularized non-linear classifier (k-Nearest Neighbors, k-NN) were comparatively tested in the present study.
The q-Gaussian RBFNN was compared to RBFNNs with Gaussian, Cauchy and inverse multiquadratic RBFs in the hidden layer and to other probabilistic classifiers, including different RBFNN design methods, support vector machines (SVMs), a sparse classifier (sparse multinomial logistic regression, SMLR) and a non-sparse classifier (regularized multinomial logistic regression, RMLR).
For protein function prediction, we developed a novel method, called regularized non-negative matrix factorization (RNMF), to seek a matrix factorization which respect the attribute features, latent graph, and unlabeled data for classification prediction.
The present 3D developments include a novel regularized approach to the contact sensitivity problem that addresses the non-differentiability of the contact constraints.
A Gaussian regularized dipolar model has been developed for carbon sp2 structures.
The proposed method effectively incorporates a priori statistical feature of intensity distribution and a modified distance regularized level set (MDRLS) method to extract object organs from CT image.
Core samples were extracted from the walls and then regularized to perform a non-standard compression test in the laboratory.
Specifically, we address representatives selection as a row-sparsity regularized minimization problem which can be effectively solved via convex programming.
To this end we introduce a sort of "regularized" version to reduce possible overfitting problems in the learning process.
