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» Evaluating algorithms that learn from data streams
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NLPRS
2001
Springer
15 years 9 months ago
A Separate-and-Learn Approach to EM Learning of PCFGs
WeproposeanewapproachtoEMlearning of PCFGs. We completely separate the process of EM learning from that of parsing, andfor theformer, weintroduce a new EM algorithm called the gra...
Taisuke Sato, Shigeru Abe, Yoshitaka Kameya, Kiyoa...
161
Voted
ICDE
2009
IEEE
143views Database» more  ICDE 2009»
15 years 11 months ago
Supporting Generic Cost Models for Wide-Area Stream Processing
— Existing stream processing systems are optimized for a specific metric, which may limit their applicability to diverse applications and environments. This paper presents XFlow...
Olga Papaemmanouil, Ugur Çetintemel, John J...
VLSID
2007
IEEE
97views VLSI» more  VLSID 2007»
15 years 10 months ago
Embedded Support Vector Machine : Architectural Enhancements and Evaluation
In recent years, research and development in the field of machine learning and classification techniques have gained paramount importance. The future generation of intelligent e...
Soumyajit Dey, Monu Kedia, Niket Agarwal, Anupam B...
KDD
2010
ACM
247views Data Mining» more  KDD 2010»
15 years 6 months ago
Active learning for biomedical citation screening
Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe ...
Byron C. Wallace, Kevin Small, Carla E. Brodley, T...
ICASSP
2009
IEEE
15 years 11 months ago
A variational EM algorithm for learning eigenvoice parameters in mixed signals
We derive an efficient learning algorithm for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a pri...
Ron J. Weiss, Daniel P. W. Ellis