The following pages link to (Q4770512):
Displaying 50 items.
- Optimal linear discriminators for the discrete choice model in growing dimensions (Q2073710) (← links)
- Distribution-free robust linear regression (Q2113267) (← links)
- Least squares approach to K-SVCR multi-class classification with its applications (Q2163855) (← links)
- Algebraic machine learning: emphasis on efficiency (Q2171721) (← links)
- Complete statistical theory of learning (Q2173177) (← links)
- Aggregation of estimators and stochastic optimization (Q2197367) (← links)
- Learning privately with labeled and unlabeled examples (Q2223696) (← links)
- Construction of confidence absorbing sets using statistical methods (Q2229546) (← links)
- Topological properties of measurable structures and sufficient conditions for uniform convergence of frequencies to probabilities (Q2261712) (← links)
- Optimal filters with multiresolution apertures (Q2273321) (← links)
- Randomized machine learning procedures (Q2289052) (← links)
- When are epsilon-nets small? (Q2304628) (← links)
- Efficient decision trees for multi-class support vector machines using entropy and generalization error estimation (Q2316469) (← links)
- Mean estimation and regression under heavy-tailed distributions: A survey (Q2329044) (← links)
- An improvement on parametric \(\nu\)-support vector algorithm for classification (Q2329896) (← links)
- Sampling bias correction in the model of mixtures with varying concentrations (Q2340310) (← links)
- Local Rademacher complexities and oracle inequalities in risk minimization. (2004 IMS Medallion Lecture). (With discussions and rejoinder) (Q2373576) (← links)
- Model selection by bootstrap penalization for classification (Q2384135) (← links)
- Fast learning rates in statistical inference through aggregation (Q2388975) (← links)
- Spline smoothing for experimental data under zero median of the noise (Q2401040) (← links)
- Recursive aggregation of estimators by the mirror descent algorithm with averaging (Q2432961) (← links)
- On using physico-chemical properties of amino acids in string kernels for protein classification via support vector machines (Q2517119) (← links)
- Mathematical methods of randomized machine learning (Q2662930) (← links)
- On the number of faces and radii of cells induced by Gaussian spherical tessellations (Q2667044) (← links)
- Robust and distributionally robust optimization models for linear support vector machine (Q2676336) (← links)
- On mean estimation for heteroscedastic random variables (Q2686600) (← links)
- Analysis of the generalization ability of a full decision tree (Q2940504) (← links)
- (Q3148814) (← links)
- Adaptive regression estimation with multilayer feedforward neural networks (Q3369526) (← links)
- (Q3799651) (← links)
- Transformation method in the classification problem (Q3804024) (← links)
- A 32-point n=12, d=5 code (Corresp.) (Q3843975) (← links)
- (Q4230622) (← links)
- On complexity of minimization and compression problems for models of sequential choice (Q4268324) (← links)
- Vapnik–Chervonenkis dimension of axis-parallel cuts (Q4563542) (← links)
- (Q4614114) (← links)
- Optimization Methods for Large-Scale Machine Learning (Q4641709) (← links)
- NEW DECISION RULES IN STATISTICAL PATTERN RECOGNITION (Q4727986) (← links)
- (Q5054630) (← links)
- Concentration Inequalities for Samples without Replacement (Q5369328) (← links)
- Variance-based regularization with convex objectives (Q5381122) (← links)
- A tutorial on <i>ν</i>‐support vector machines (Q5467277) (← links)
- Optimal estimation for large-eddy simulation of turbulence and application to the analysis of subgrid models (Q5756127) (← links)
- Fisher’s Conditionality Principle in Statistical Pattern Recognition (Q5885305) (← links)
- Deep learning: a statistical viewpoint (Q5887827) (← links)
- Newton-based approach to solving K-SVCR and twin-KSVC multi-class classification in the primal space (Q6068698) (← links)
- A Lagrangian-based approach for universum twin bounded support vector machine with its applications (Q6113059) (← links)
- The deep arbitrary polynomial chaos neural network or how deep artificial neural networks could benefit from data-driven homogeneous chaos theory (Q6488834) (← links)
- Realizable learning is all you need (Q6566462) (← links)
- Network-based semisupervised clustering (Q6579523) (← links)
