Machine learning
298 methods.
Machine learning
228 methods
- Active learning Association rules
- Active Learning Autoencoder Anomaly Detection
- Active learning Boosting
- Active learning Decision tree
- Active Learning Federated Learning
- Active learning Gaussian mixture model
- Active learning Gaussian process
- Active Learning Gradient Boosting
- Active learning Isolation forest
- Active learning K-nearest neighbors
- Active Learning LightGBM
- Active Learning Linear Regression
- Active Learning Logistic Regression
- Active learning One-class SVM
- Active Learning Self-supervised Learning
- Active learning Stacking ensemble
- Active learning Support vector machine
- Active Learning Voting Ensemble
- Apriori Algorithm
- Association Rules
- Autoencoder Anomaly Detection
- Bayesian Active Learning
- Bayesian Association Rules
- Bayesian Autoencoder Anomaly Detection
- Bayesian Bagging
- Bayesian Boosting
- Bayesian Decision Tree
- Bayesian Federated Learning
- Bayesian Few-Shot Learning
- Bayesian Gaussian Mixture Model
- Bayesian Gaussian Process
- Bayesian k-nearest neighbors
- Bayesian LightGBM
- Bayesian Metric Learning
- Bayesian Naive Bayes
- Bayesian one-class SVM
- Bayesian Online Learning
- Bayesian Random Forest
- Bayesian Semi-supervised Learning
- Bayesian Stacking Ensemble
- Bayesian Support Vector Machine
- Bayesian Transfer Learning
- Bayesian XGBoost
- Boosting
- Ensemble Active Learning
- Ensemble Apriori Algorithm
- Ensemble Association Rules
- Ensemble Autoencoder Anomaly Detection
- Ensemble Decision Tree
- Ensemble Federated Learning
- Ensemble Few-shot learning
- Ensemble Gaussian Mixture Model
- Ensemble Gaussian Process
- Ensemble Gradient Boosting
- Ensemble HDBSCAN
- Ensemble Isolation Forest
- Ensemble K-means
- Ensemble K-nearest neighbors
- Ensemble Linear Regression
- Ensemble Logistic Regression
- Ensemble Metric Learning
- Ensemble Naive Bayes
- Ensemble One-class SVM
- Ensemble Online Learning
- Ensemble Self-supervised Learning
- Ensemble Semi-supervised Learning
- Ensemble Support Vector Machine
- Ensemble Transfer Learning
- Explainable Association Rules
- Explainable Autoencoder Anomaly Detection
- Explainable DBSCAN
- Explainable Decision Tree
- Explainable Extra Trees
- Explainable FP-Growth
- Explainable Gaussian Mixture Model
- Explainable Gaussian Process
- Explainable Gradient Boosting
- Explainable HDBSCAN
- Explainable Isolation Forest
- Explainable K-Means
- Explainable K-Nearest Neighbors
- Explainable LightGBM
- Explainable Naive Bayes
- Explainable One-Class SVM
- Explainable Random Forest
- Explainable Stacking Ensemble
- Explainable Support Vector Machine
- Explainable Voting Ensemble
- Explainable XGBoost
- Extra Trees
- Few-shot Learning
- Gaussian Process
- K-means
- Linear Regression (ML)
- Logistic regression (ML)
- Metric Learning
- One-class SVM
- Online Active learning
- Online Association Rules
- Online Autoencoder Anomaly Detection
- Online Bagging
- Online Boosting
- Online DBSCAN
- Online Decision Tree
- Online Federated Learning
- Online Few-shot Learning
- Online FP-growth
- Online Gaussian Mixture Model
- Online Gaussian Process
- Online Gradient Boosting
- Online HDBSCAN
- Online Isolation Forest
- Online K-means
- Online K-nearest neighbors
- Online Learning
- Online LightGBM
- Online Linear Regression
- Online Logistic Regression
- Online Metric Learning
- Online Naive Bayes
- Online One-class SVM
- Online Random Forest
- Online Self-supervised Learning
- Online Semi-supervised learning
- Online Support Vector Machine
- Online Transfer learning
- Online Voting Ensemble
- Regularized Boosting
- Regularized CatBoost
- Regularized Decision Tree
- Regularized Federated Learning
- Regularized Few-Shot Learning
- Regularized Gaussian Mixture Model
- Regularized Gaussian Process
- Regularized Gradient Boosting
- Regularized k-means
- Regularized k-nearest neighbors
- Regularized LightGBM
- Regularized linear regression
- Regularized Logistic Regression
- Regularized Naive Bayes
- Regularized Online Learning
- Regularized random forest
- Regularized semi-supervised learning
- Regularized Stacking Ensemble
- Regularized Support Vector Machine
- Regularized Transfer Learning
- Robust Active Learning
- Robust Autoencoder anomaly detection
- Robust Bagging
- Robust Boosting
- Robust Decision Tree
- Robust Federated Learning
- Robust Gaussian Mixture Model
- Robust Gaussian Process
- Robust Gradient Boosting
- Robust HDBSCAN
- Robust Isolation forest
- Robust k-means
- Robust LightGBM
- Robust Linear Regression
- Robust Metric Learning
- Robust Naive Bayes
- Robust One-class SVM
- Robust Online Learning
- Robust Random Forest
- Robust Stacking Ensemble
- Robust Support Vector Machine
- Robust Voting Ensemble
- Robust XGBoost
- Self-supervised Active Learning
- Self-supervised Autoencoder Anomaly Detection
- Self-supervised Boosting
- Self-supervised DBSCAN
- Self-supervised Decision Tree
- Self-supervised Federated learning
- Self-supervised Few-shot Learning
- Self-supervised Gaussian Mixture Model
- Self-supervised Gaussian Process
- Self-supervised Gradient Boosting
- Self-supervised Isolation Forest
- Self-supervised K-means
- Self-supervised K-nearest neighbors
- Self-supervised Learning
- Self-supervised LightGBM
- Self-supervised Logistic Regression
- Self-supervised Metric learning
- Self-supervised Naive Bayes
- Self-supervised One-class SVM
- Self-supervised Random Forest
- Self-supervised Stacking Ensemble
- Self-supervised Support Vector Machine
- Self-supervised Transfer learning
- Semi-supervised Active Learning
- Semi-supervised Apriori Algorithm
- Semi-supervised Association Rules
- Semi-supervised Autoencoder Anomaly Detection
- Semi-supervised Bagging
- Semi-supervised Boosting
- Semi-supervised CatBoost
- Semi-supervised DBSCAN
- Semi-supervised Decision Tree
- Semi-supervised Federated learning
- Semi-supervised Few-shot Learning
- Semi-supervised FP-growth
- Semi-supervised Gaussian Mixture Model
- Semi-supervised Gaussian Process
- Semi-supervised Gradient Boosting
- Semi-supervised HDBSCAN
- Semi-supervised Isolation Forest
- Semi-supervised K-means
- Semi-supervised K-nearest neighbors
- Semi-supervised Learning
- Semi-supervised LightGBM
- Semi-supervised Linear Regression
- Semi-supervised Logistic Regression
- Semi-supervised Metric Learning
- Semi-supervised Naive Bayes
- Semi-supervised One-class SVM
- Semi-supervised Online Learning
- Semi-supervised Random Forest
- Semi-supervised Stacking Ensemble
- Semi-supervised Support Vector Machine
- Semi-supervised Transfer Learning
- Semi-supervised Voting Ensemble
- Semi-supervised XGBoost
- Transfer Learning
- Voting Ensemble
Ml-model
42 methods
- AdaBoost
- Affinity Propagation
- Bagging
- BIRCH
- CatBoost
- DBSCAN
- Decision Tree
- Elastic Net
- Gaussian Mixture Model
- Generalized Additive Model
- Gradient Boosting
- HDBSCAN
- Hierarchical Clustering
- Isolation Forest
- K-Means Clustering
- K-Nearest Neighbors
- Label Propagation
- Lasso Regression
- LightGBM
- Local Outlier Factor
- Locally Linear Embedding
- LOESS
- MARS
- Mean Shift
- Multi-layer Perceptron
- Naive Bayes
- OPTICS
- Partial Least Squares
- Principal Component Analysis
- Principal Components Regression
- Random Forest
- Regression Splines
- Ridge Regression
- SHAP
- Spectral Clustering
- Stacking
- Stochastic Gradient Descent
- Support Vector Machine
- Support Vector Regression
- t-SNE
- UMAP
- XGBoost
Latent-structure
7 methods
Pattern mining
5 methods
Trustworthy ML
4 methods
Explainable AI
2 methods
Reinforcement learning
2 methods
Dimensionality reduction
2 methods
Interactive ML
1 method
Bayesian
1 method
Recommender systems
1 method
Clustering
1 method
Missing data
1 method
Rule learning
1 method