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MCS
2007
Springer
15 years 3 months ago
An Ensemble Approach for Incremental Learning in Nonstationary Environments
Abstract. We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presente...
Michael Muhlbaier, Robi Polikar
GECCO
2006
Springer
159views Optimization» more  GECCO 2006»
15 years 1 months ago
Identification of weak motifs in multiple biological sequences using genetic algorithm
Recognition of motifs in multiple unaligned sequences provides an insight into protein structure and function. The task of discovering these motifs is very challenging because mos...
Topon Kumar Paul, Hitoshi Iba
PCI
2005
Springer
15 years 3 months ago
Protein Classification with Multiple Algorithms
Nowadays, the number of protein sequences being stored in central protein databases from labs all over the world is constantly increasing. From these proteins only a fraction has b...
Sotiris Diplaris, Grigorios Tsoumakas, Pericles A....
CSL
2010
Springer
14 years 9 months ago
Active learning and semi-supervised learning for speech recognition: A unified framework using the global entropy reduction maxi
We propose a unified global entropy reduction maximization (GERM) framework for active learning and semi-supervised learning for speech recognition. Active learning aims to select...
Dong Yu, Balakrishnan Varadarajan, Li Deng, Alex A...
ICML
2005
IEEE
15 years 10 months ago
Preference learning with Gaussian processes
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relat...
Wei Chu, Zoubin Ghahramani