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ICTAI
2007
IEEE
15 years 6 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...
ECCV
2010
Springer
15 years 4 months ago
Weakly Supervised Shape Based Object Detection with Particle Filter
Abstract. We describe an efficient approach to construct shape models composed of contour parts with partially-supervised learning. The proposed approach can easily transfer parts ...
CORR
2011
Springer
213views Education» more  CORR 2011»
14 years 6 months ago
Adapting to Non-stationarity with Growing Expert Ensembles
Forecasting sequences by expert ensembles generally assumes stationary or near-stationary processes; however, in complex systems and many real-world applications, we are frequentl...
Cosma Rohilla Shalizi, Abigail Z. Jacobs, Aaron Cl...
PAMI
2006
138views more  PAMI 2006»
14 years 11 months ago
Context-Based Segmentation of Image Sequences
We describe an algorithm for context-based segmentation of visual data. New frames in an image sequence (video) are segmented based on the prior segmentation of earlier frames in ...
Jacob Goldberger, Hayit Greenspan
ADMA
2006
Springer
110views Data Mining» more  ADMA 2006»
15 years 3 months ago
Learning with Local Drift Detection
Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
João Gama, Gladys Castillo