The following pages link to UCI-ml (Q16261):
Displaying 50 items.
- Cost-sensitive boosting for classification of imbalanced data (Q996413) (← links)
- Fuzzy classifier design using genetic algorithms (Q996420) (← links)
- Decision trees using model ensemble-based nodes (Q996440) (← links)
- EROS: Ensemble rough subspaces (Q996469) (← links)
- Parallelization of fuzzy ARTMAP to improve its convergence speed: the network partitioning approach and the data partitioning approach (Q999617) (← links)
- New multicategory boosting algorithms based on multicategory Fisher-consistent losses (Q999662) (← links)
- A weakly informative default prior distribution for logistic and other regression models (Q999667) (← links)
- Bayesian multinomial regression with class-specific predictor selection (Q999673) (← links)
- Optimal kernel selection in twin support vector machines (Q1001328) (← links)
- Optimizing logistic regression coefficients for discrimination and calibration using estimation of distribution algorithms (Q1001363) (← links)
- An incremental algorithm to construct a lattice of set intersections (Q1001808) (← links)
- A new feature selection method for Gaussian mixture clustering (Q1005612) (← links)
- Learning from partially supervised data using mixture models and belief functions (Q1005634) (← links)
- Subspace projection: A unified framework for a class of partition-based dimension reduction techniques (Q1006743) (← links)
- Moving towards efficient decision tree construction (Q1006753) (← links)
- A new measure of uncertainty based on knowledge granulation for rough sets (Q1007836) (← links)
- Data gravitation based classification (Q1007885) (← links)
- On kernel difference-weighted \(k\)-nearest neighbor classification (Q1008943) (← links)
- Distance-based discriminant analysis method and its applications (Q1008946) (← links)
- Efficient tree traversal to reduce code growth in tree-based genetic programming (Q1009203) (← links)
- Feature selection via sensitivity analysis of SVM probabilistic outputs (Q1009227) (← links)
- Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm (Q1009249) (← links)
- A \(k\)-norm pruning algorithm for decision tree classifiers based on error rate estimation (Q1009253) (← links)
- Layered critical values: a powerful direct-adjustment approach to discovering significant patterns (Q1009257) (← links)
- On reoptimizing multi-class classifiers (Q1009262) (← links)
- Improved MCMC sampling methods for estimating weighted sums in Winnow with application to DNF learning (Q1009287) (← links)
- Boosted Bayesian network classifiers (Q1009291) (← links)
- Discretization for Naive-Bayes learning: managing discretization bias and variance (Q1009306) (← links)
- Classifying under computational resource constraints: anytime classification using probabilistic estimators (Q1009341) (← links)
- Novel approaches to probabilistic neural networks through bagging and evolutionary estimating of prior probabilities (Q1009347) (← links)
- A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems (Q1010069) (← links)
- Exploring the boundary region of tolerance rough sets for feature selection (Q1010071) (← links)
- A scalable framework for cluster ensembles (Q1010074) (← links)
- A multi-prototype clustering algorithm (Q1010075) (← links)
- An efficient discriminant-based solution for small sample size problem (Q1010095) (← links)
- Efficient single-pass frequent pattern mining using a prefix-tree (Q1010129) (← links)
- Nonparallel plane proximal classifier (Q1010181) (← links)
- Data analysis with fuzzy clustering methods (Q1010356) (← links)
- Analysis of new variable selection methods for discriminant analysis (Q1010495) (← links)
- Gaussian kernel optimization for pattern classification (Q1015178) (← links)
- A simultaneous learning framework for clustering and classification (Q1015179) (← links)
- Data dependency in multiple classifier systems (Q1015182) (← links)
- Feature selection with dynamic mutual information (Q1015191) (← links)
- A closed-form reduction of multi-class cost-sensitive learning to weighted multi-class learning (Q1015231) (← links)
- A method for improving the accuracy of data mining classification algorithms (Q1017461) (← links)
- On universal transfer learning (Q1017661) (← links)
- Parallelizing AdaBoost by weights dynamics (Q1019879) (← links)
- A stochastic approximation view of boosting (Q1020818) (← links)
- Model selection for support vector machines via uniform design (Q1020820) (← links)
- DIVCLUS-T: a monothetic divisive hierarchical clustering method (Q1020864) (← links)
