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» New ensemble methods for evolving data streams
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PAKDD
2004
ACM
137views Data Mining» more  PAKDD 2004»
14 years 2 months ago
Fast and Light Boosting for Adaptive Mining of Data Streams
Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept dr...
Fang Chu, Carlo Zaniolo
FUZZIEEE
2007
IEEE
14 years 19 days ago
Evolving Single- and Multi-Model Fuzzy Classifiers with FLEXFIS-Class
Abstract-- In this paper a new method for training singlemodel and multi-model fuzzy classifiers incrementally and adaptively is proposed, which is called FLEXFIS-Class. The evolvi...
Edwin Lughofer, Plamen P. Angelov, Xiaowei Zhou
PAKDD
2010
ACM
212views Data Mining» more  PAKDD 2010»
14 years 1 months 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...
CN
2006
163views more  CN 2006»
13 years 8 months ago
A framework for mining evolving trends in Web data streams using dynamic learning and retrospective validation
The expanding and dynamic nature of the Web poses enormous challenges to most data mining techniques that try to extract patterns from Web data, such as Web usage and Web content....
Olfa Nasraoui, Carlos Rojas, Cesar Cardona
SDM
2004
SIAM
218views Data Mining» more  SDM 2004»
13 years 10 months ago
Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
Ashok N. Srivastava