Olindda: a cluster-based approach for detecting novelty and concept drift in data streams (2007)
- Authors:
- Autor USP: CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC
- Unidade: ICMC
- Assunto: COMPUTAÇÃO BIOINSPIRADA
- Language: Inglês
- Imprenta:
- ISBN: 159593480-4
- Source:
- Título do periódico: Proceedings
- Conference titles: Annual ACM Symposium on Applied Computing'2007
-
ABNT
SPINOSA, Eduardo J.; CARVALHO, André Carlos Ponce de Leon Ferreira; GAMA, João. Olindda: a cluster-based approach for detecting novelty and concept drift in data streams. Anais.. New York: ACM, 2007. -
APA
Spinosa, E. J., Carvalho, A. C. P. de L. F., & Gama, J. (2007). Olindda: a cluster-based approach for detecting novelty and concept drift in data streams. In Proceedings. New York: ACM. -
NLM
Spinosa EJ, Carvalho ACP de LF, Gama J. Olindda: a cluster-based approach for detecting novelty and concept drift in data streams. Proceedings. 2007 ; -
Vancouver
Spinosa EJ, Carvalho ACP de LF, Gama J. Olindda: a cluster-based approach for detecting novelty and concept drift in data streams. Proceedings. 2007 ; - Hybrid classification algorithms based on boosting and support vector machines
- A comparison of hierarchical biclustering ensemble methods
- Unsupervised domain adaptation for human activity recognition
- Recommending collaborative filtering algorithms using subsampling landmarkers
- Fast adaptive stacking of ensembles
- Automatic design of decision-tree induction algorithms
- To tune or not to tune: recommending when to adjust SVM hyper-parameters via meta-learning
- Evolutionary radial basis functions for credit assessment
- Neural networks for hierarchical classification of G-Protein Coupled Receptors
- Multi-label feature selection techniques for hierarchical multi-label protein function prediction
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