Ensemble Methods: Foundations and Algorithms
Explore this paper's citation graph
Summary
This chapter discussessemble methods, which train multiple learners and then combine them for use to boost weak learners, which are even just slightly better than random performance to strong learners, who can make very accurate predictions.
- Published
- 2012-01-01
- Cited by
- 2,162
- References
- 0
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15096071
References
No references recorded for this paper.
Cited by
- Predicting Treatment Relations with Semantic Patterns over Biomedical Knowledge Graphs
- Computational analytics for venture finance
- Machine Learning Methods for High-Dimensional Imbalanced Biomedical Data
- Intelligent techniques for recommender systems
- The Spaces of Data, Information, and Knowledge
- Context-based ensemble method for human energy expenditure estimation
- Argumentation Based Joint Learning: A Novel Ensemble Learning Approach
- Dynamic texture recognition by aggregating spatial and temporal features via ensemble SVMs
- Evaluating trade‐offs in energy‐efficient error detection
- Machine Learning - The Art and Science of Algorithms that Make Sense of Data
- Fusing Monotonic Decision Trees
- Comparative study of classifier ensembles for cost-sensitive credit risk assessment
- Fine-grained maize tassel trait characterization with multi-view representations
- An Anomaly Detection System Based on Ensemble of Detectors with Effective Pruning Techniques
- Classification of clinical outcomes using high-throughput and clinical informatics.
- Banzhaf Random Forests
- Model recommendation: Generating object detectors from few samples
- Using metaheuristics to optimize the combination of classifier and cluster ensembles
- Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air
- An ensemble approach to estimate the fault-time instant
Related papers
No related papers recorded.