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Found 10 papers, 1 papers with code
Ontology-based Solution for Building an Intelligent Searching System on Traffic Law Documents
In this paper, an ontology-based approach is used to organize the knowledge base of legal documents in road traffic law.
Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts
Mixture of experts (MoE) has a well-principled finite mixture model construction for prediction, allowing the gating network (mixture weights) to learn from the predictors (explanatory variables) together with the experts' network (mixture component densities).
Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models
Mixtures-of-Experts models and their maximum likelihood estimation (MLE) via the EM algorithm have been thoroughly studied in the statistics and machine learning literature.
Approximate Bayesian computation via the energy statistic
Furthermore, we propose a consistent V-statistic estimator of the energy statistic, under which we show that the large sample result holds, and prove that the rejection ABC algorithm, based on the energy statistic, generates pseudo-posterior distributions that achieves convergence to the correct limits, when implemented with rejection thresholds that converge to zero, in the finite sample setting.
Partial Evaluation of Logic Programs in Vector Spaces
In this paper, we introduce methods of encoding propositional logic programs in vector spaces.
Model-Based Clustering and Classification of Functional Data
FDA is the data analysis paradigm in which the individual data units are functions (e. g., curves, surfaces), rather than simple vectors.
An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling
Due to the probabilistic nature of MoE models, we propose the maximum quasi-likelihood (MQL) estimator as a method for estimating MoE model parameters from data, and we provide conditions under which MQL estimators are consistent and asymptotically normal.
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach
Support vector machines (SVMs) are an important tool in modern data analysis.
An Introduction to MM Algorithms for Machine Learning and Statistical
MM (majorization--minimization) algorithms are an increasingly popular tool for solving optimization problems in machine learning and statistical estimation.
A Universal Approximation Theorem for Mixture of Experts Models
The mixture of experts (MoE) model is a popular neural network architecture for nonlinear regression and classification.
