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ICTAI
2007
IEEE
13 years 11 months ago
An Adaptive Distributed Ensemble Approach to Mine Concept-Drifting Data Streams
An adaptive boosting ensemble algorithm for classifying homogeneous distributed data streams is presented. The method builds an ensemble of classifiers by using Genetic Programmi...
Gianluigi Folino, Clara Pizzuti, Giandomenico Spez...
AAAI
1994
13 years 6 months ago
Automated Accompaniment of Musical Ensembles
' This paper describes a computer accompaniment system capable of providing musical accompaniment for an ensemble of performers. The system tracks the performance of each musi...
Lorin Grubb, Roger B. Dannenberg
CVPR
2007
IEEE
13 years 8 months ago
Object Classification in Visual Surveillance Using Adaboost
In this paper, we present a method of object classification within the context of Visual Surveillance. Our goal is the classification of tracked objects into one of the two classe...
John-Paul Renno, Dimitrios Makris, Graeme A. Jones
ICDM
2003
IEEE
181views Data Mining» more  ICDM 2003»
13 years 10 months ago
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
Algorithms for tracking concept drift are important for many applications. We present a general method based on the Weighted Majority algorithm for using any online learner for co...
Jeremy Z. Kolter, Marcus A. Maloof
BC
2007
107views more  BC 2007»
13 years 4 months ago
Decoding spike train ensembles: tracking a moving stimulus
We consider the issue of how to read out the information from nonstationary spike train ensembles. Based on the theory of censored data in statistics, we propose a ‘censored’ m...
Enrico Rossoni, Jianfeng Feng