The following pages link to UCI-ml (Q16261):
Displaying 50 items.
- Growing support vector classifiers with controlled complexity. (Q1403745) (← links)
- Recursive update algorithm for least squares support vector machines (Q1404283) (← links)
- Learning classification rules from data. (Q1416436) (← links)
- Relaxed conditions for radial-basis function networks to be universal approximators. (Q1422258) (← links)
- Standardizing the comparison of partitions (Q1424639) (← links)
- Active sampling for class probability estimation and ranking (Q1424773) (← links)
- Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density. (Q1425932) (← links)
- Vote counting measures for ensemble classifiers. (Q1425964) (← links)
- An approach to generate rules from neural networks for regression problems. (Q1428066) (← links)
- Mining data to find subsets of high activity. (Q1429873) (← links)
- Combining boosting and evolutionary algorithms for learning of fuzzy classification rules. (Q1430851) (← links)
- Theoretical and empirical analysis of ReliefF and RReliefF (Q1431692) (← links)
- Unsupervised and supervised data classification via nonsmooth and global optimization (with comments and rejoinder) (Q1433467) (← links)
- Fuzzy rule based classification with FeatureSelector and modified threshold accepting (Q1577125) (← links)
- Formal methods in pattern recognition: A review (Q1579483) (← links)
- Feature Subset Selection by Bayesian network-based optimization (Q1589473) (← links)
- Minimal approximate hitting sets and rule templates (Q1594848) (← links)
- Using table lens to interactively build classifiers (Q1600572) (← links)
- Learning good prototypes for classification using filtering and abstraction of instances (Q1601566) (← links)
- Hierarchical genetic fuzzy systems (Q1602475) (← links)
- An experimental study about the search mechanism in the SLAVE learning algorithm: Hill-climbing methods versus genetic algorithms (Q1602488) (← links)
- A geometric approach to leveraging weak learners (Q1603593) (← links)
- Ensembling neural networks: Many could be better than all (Q1605287) (← links)
- Using \(k\)-nearest-neighbor classification in the leaves of a tree (Q1606094) (← links)
- Logical analysis of binary data with missing bits (Q1606295) (← links)
- Making use of the most expressive jumping emerging patterns for classification (Q1606553) (← links)
- Introducing IVSA: A new concept learning algorithm (Q1609147) (← links)
- Hybrid extreme point tabu search (Q1609924) (← links)
- Sequential combination methods for data clustering analysis (Q1613232) (← links)
- Effective discovery of exception class association rules (Q1613269) (← links)
- Entropy-based sliced inverse regression (Q1615091) (← links)
- Sparse high-dimensional fractional-norm support vector machine via DC programming (Q1615094) (← links)
- Asymmetric least squares support vector machine classifiers (Q1615251) (← links)
- Rough-fuzzy rule interpolation (Q1615665) (← links)
- Method for higher order polynomial Sugeno fuzzy inference systems (Q1615668) (← links)
- Deep learning model selection of suboptimal complexity (Q1616234) (← links)
- Distance measure with improved lower bound for multivariate time series (Q1620338) (← links)
- Searching for the core variables in principal components analysis (Q1620923) (← links)
- Penalized likelihood and Bayesian function selection in regression models (Q1621251) (← links)
- Model-based clustering of high-dimensional data: a review (Q1621282) (← links)
- A multivariate linear regression analysis using finite mixtures of \(t\) distributions (Q1621290) (← links)
- Learning from incomplete data via parameterized \(t\) mixture models through eigenvalue decomposition (Q1621293) (← links)
- Analysis of feature selection stability on high dimension and small sample data (Q1621349) (← links)
- Using random subspace method for prediction and variable importance assessment in linear regression (Q1621353) (← links)
- Sparse group Lasso and high dimensional multinomial classification (Q1621358) (← links)
- Estimating mutual information for feature selection in the presence of label noise (Q1621365) (← links)
- (Psycho-)analysis of benchmark experiments: a formal framework for investigating the relationship between data sets and learning algorithms (Q1621378) (← links)
- Wallenius Bayes (Q1621871) (← links)
- Improved maximum inner product search with better theoretical guarantee using randomized partition trees (Q1621880) (← links)
- Mean field variational Bayesian inference for support vector machine classification (Q1623434) (← links)
