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Found 17 papers, 2 papers with code
Learning using granularity statistical invariants for classification
Learning using statistical invariants (LUSI) is a new learning paradigm, which adopts weak convergence mechanism, and can be applied to a wider range of classification problems.
Least Squares Maximum and Weighted Generalization-Memorization Machines
Furthermore, we propose some different memory impact functions for the MIMM and WIMM.
Classification by estimating the cumulative distribution function for small data
Based on the Fredholm equation, a new expected risk estimation theory by estimating the cumulative distribution function is presented.
Generalization-Memorization Machines
Under this mechanism, error-based learning machines improve their memorization abilities of training data without over-fitting.
Nonlinear Kernel Support Vector Machine with 0-1 Soft Margin Loss
Recent advance on linear support vector machine with the 0-1 soft margin loss ($L_{0/1}$-SVM) shows that the 0-1 loss problem can be solved directly.
Fuzzy Discriminant Clustering with Fuzzy Pairwise Constraints
In semi-supervised fuzzy clustering, this paper extends the traditional pairwise constraint (i. e., must-link or cannot-link) to fuzzy pairwise constraint.
Two-dimensional Bhattacharyya bound linear discriminant analysis with its applications
Recently proposed L2-norm linear discriminant analysis criterion via the Bhattacharyya error bound estimation (L2BLDA) is an effective improvement of linear discriminant analysis (LDA) for feature extraction.
Capped norm linear discriminant analysis and its applications
To improve the robustness of LDA, in this paper, we introduce capped l_{2, 1}-norm of a matrix, which employs non-squared l_2-norm and "capped" operation, and further propose a novel capped l_{2, 1}-norm linear discriminant analysis, called CLDA.
Principal Component Analysis Based on T$\ell_1$-norm Maximization
Classical principal component analysis (PCA) may suffer from the sensitivity to outliers and noise.
Multiple Flat Projections for Cross-manifold Clustering
In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems.
Support Vector Machine Classifier via $L_{0/1}$ Soft-Margin Loss
To distinguish all of them, in this paper, we introduce a new model equipped with an $L_{0/1}$ soft-margin loss (dubbed as $L_{0/1}$-SVM) which well captures the nature of the binary classification.
Single and Union Non-parallel Support Vector Machine Frameworks
The other type constructs all the hyperplanes simultaneously, and it solves one big optimization problem with the ascertained loss of each sample.
A general model for plane-based clustering with loss function
In this paper, we propose a general model for plane-based clustering.
Ramp-based Twin Support Vector Clustering
Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost.
Robust Bhattacharyya bound linear discriminant analysis through adaptive algorithm
In this paper, we propose a novel linear discriminant analysis criterion via the Bhattacharyya error bound estimation based on a novel L1-norm (L1BLDA) and L2-norm (L2BLDA).
Generalized two-dimensional linear discriminant analysis with regularization
However, 2DLDA may encounter the singularity issue theoretically and the sensitivity to outliers.
Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problems
In this paper, stochastic gradient descent algorithm is investigated to twin support vector machines for classification.
