The following pages link to e1071 (Q20001):
Displaying 50 items.
- Biomarker discovery: classification using pooled samples (Q2255767) (← links)
- Benchmarking local classification methods (Q2259338) (← links)
- A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics (Q2259821) (← links)
- Ensemble quantile classifier (Q2291292) (← links)
- Efficient computation for differential network analysis with applications to quadratic discriminant analysis (Q2291319) (← links)
- Supervised learning via smoothed Polya trees (Q2303053) (← links)
- The \(\delta \)-machine: classification based on distances towards prototypes (Q2304085) (← links)
- A difference of convex optimization algorithm for piecewise linear regression (Q2313774) (← links)
- Development and evaluation of geostatistical methods for non-Euclidean-based spatial covariance matrices (Q2323497) (← links)
- Validation in principal components analysis applied to EEG data (Q2330157) (← links)
- Characterization of between-group inequality of longevity in European union countries (Q2364017) (← links)
- Evolutionary support vector regression algorithm applied to the prediction of the thickness of the chromium layer in a hard chromium plating process (Q2396453) (← links)
- Bayesian classification for bivariate normal gene expression (Q2445655) (← links)
- On a strategy to develop robust and simple tariffs from motor vehicle insurance data (Q2508010) (← links)
- Testing serial independence with functional data (Q2666064) (← links)
- Unobserved classes and extra variables in high-dimensional discriminant analysis (Q2673359) (← links)
- Group-wise shrinkage estimation in penalized model-based clustering (Q2680189) (← links)
- Constructive regression on implicit regions (Q2795850) (← links)
- Studying Complexity of Model-based Clustering (Q2816737) (← links)
- Foundations of statistical algorithms. With references to R packages (Q2871231) (← links)
- Applied Predictive Modeling (Q2874155) (← links)
- Six Sigma with R (Q2880230) (← links)
- Modern data science with R (Q2959035) (← links)
- Classification Based on the Support Vector Machine, Regression Depth, and Discriminant Analysis (Q3298682) (← links)
- Measuring the Stability of Results From Supervised Statistical Learning (Q3391150) (← links)
- Scalable Bayesian Nonparametric Clustering and Classification (Q3391447) (← links)
- Diagonal Discriminant Analysis With Feature Selection for High-Dimensional Data (Q3391457) (← links)
- Estimating the Number of Clusters Using Cross-Validation (Q3391464) (← links)
- (Q3463720) (← links)
- Applied Spatial Data Analysis with R (Q3503423) (← links)
- A comparison of classification models to identify the Fragile X Syndrome (Q3532659) (← links)
- (Q4558532) (← links)
- Semiparametric Regression with R (Q4623082) (← links)
- Classification via Bayesian Nonparametric Learning of Affine Subspaces (Q4916938) (← links)
- (Q4969042) (← links)
- Noise accumulation in high dimensional classification and total signal index (Q4969076) (← links)
- APPLYING ECONOMIC MEASURES TO LAPSE RISK MANAGEMENT WITH MACHINE LEARNING APPROACHES (Q5019041) (← links)
- Classification using semiparametric mixtures (Q5034169) (← links)
- A comparison of machine learning techniques for taxonomic classification of teeth from the Family Bovidae (Q5036352) (← links)
- Hole or Grain? A Section Pursuit Index for Finding Hidden Structure in Multiple Dimensions (Q5057084) (← links)
- Directional Quantile Classifiers (Q5057099) (← links)
- Robust piecewise linear <i>L</i><sub>1</sub>-regression via nonsmooth DC optimization (Q5058373) (← links)
- Diverse classifiers ensemble based on GMDH-type neural network algorithm for binary classification (Q5082990) (← links)
- An Introduction to Clustering with R (Q5119497) (← links)
- (Q5125160) (← links)
- Supervised Machine Learning (Q5125274) (← links)
- Bayes Rules! (Q5163774) (← links)
- Data Mining Algorithms (Q5173154) (← links)
- Hands-On Machine Learning with R (Q5206307) (← links)
- (Q5211853) (← links)
