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PAKDD
2010
ACM
212views Data Mining» more  PAKDD 2010»
14 years 2 days ago
Fast Perceptron Decision Tree Learning from Evolving Data Streams
Abstract. Mining of data streams must balance three evaluation dimensions: accuracy, time and memory. Excellent accuracy on data streams has been obtained with Naive Bayes Hoeffdi...
Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer...
FLAIRS
2004
13 years 8 months ago
The Optimality of Naive Bayes
Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surpris...
Harry Zhang
ICPR
2010
IEEE
14 years 29 days ago
On-Line Random Naive Bayes for Tracking
—Randomized learning methods (i.e., Forests or Ferns) have shown excellent capabilities for various computer vision applications. However, it was shown that the tree structure in...
Martin Godec, Christian Leistner, Amir Saffari, Ho...
ICMLA
2007
13 years 8 months ago
Learning bayesian networks consistent with the optimal branching
We introduce a polynomial-time algorithm to learn Bayesian networks whose structure is restricted to nodes with in-degree at most k and to edges consistent with the optimal branch...
Alexandra M. Carvalho, Arlindo L. Oliveira
SAC
2005
ACM
14 years 26 days ago
Learning decision trees from dynamic data streams
: This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental a...
João Gama, Pedro Medas, Pedro Pereira Rodri...