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Found 4 papers, 1 papers with code
Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models
The contrastive divergence algorithm is a popular approach to training energy-based latent variable models, which has been widely used in many machine learning models such as the restricted Boltzmann machines and deep belief nets.
Distance-weighted Support Vector Machine
The proposed Distance-weighted Support Vector Machine method can be viewed as a hybrid of SVM and DWD that finds the classification direction by minimizing mainly the DWD loss, and determines the intercept term in the SVM manner.
Flexible High-dimensional Classification Machines and Their Asymptotic Properties
Simulations and real data applications are investigated to illustrate the usefulness of the FLAME classifiers.
Nested Nonnegative Cone Analysis
The application of traditional PCA/SVD method to nonnegative data often cause the approximation matrix leave the nonnegative cone, which leads to non-interpretable and sometimes nonsensical results.
