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» The Use of Classifiers in Sequential Inference
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SIGIR
2002
ACM
13 years 7 months ago
Unsupervised document classification using sequential information maximization
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential ...
Noam Slonim, Nir Friedman, Naftali Tishby
ICRA
2009
IEEE
94views Robotics» more  ICRA 2009»
14 years 2 months ago
Inferring a probability distribution function for the pose of a sensor network using a mobile robot
— In this paper we present an approach for localizing a sensor network augmented with a mobile robot which is capable of providing inter-sensor pose estimates through its odometr...
David Meger, Dimitri Marinakis, Ioannis M. Rekleit...
KDD
2003
ACM
148views Data Mining» more  KDD 2003»
14 years 7 months ago
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
JMLR
2010
156views more  JMLR 2010»
13 years 2 months ago
Classification with Incomplete Data Using Dirichlet Process Priors
A non-parametric hierarchical Bayesian framework is developed for designing a classifier, based on a mixture of simple (linear) classifiers. Each simple classifier is termed a loc...
Chunping Wang, Xuejun Liao, Lawrence Carin, David ...
ICASSP
2011
IEEE
12 years 11 months ago
Sequential Monte Carlo method for parameter estimation in diffusion models of affinity-based biosensors
Estimation of the amounts of target molecules in realtime affinity-based biosensors is studied. The problem is mapped to inferring the parameters of a temporally sampled diffusio...
Manohar Shamaiah, Xiaohu Shen, Haris Vikalo