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» Evaluating algorithms that learn from data streams
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KDD
2003
ACM
148views Data Mining» more  KDD 2003»
16 years 1 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
ICPR
2008
IEEE
15 years 7 months ago
Learning combined similarity measures from user data for image retrieval
Image retrieval has become an interesting and active field due to the increasing necessity of searching and browsing very large image repositories. Images are represented using s...
Miguel Arevalillo-Herráez, Francesc J. Ferr...
UAI
1998
15 years 1 months ago
A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data
In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe ...
Stefano Monti, Gregory F. Cooper
97
Voted
AIED
2005
Springer
15 years 6 months ago
Inferring learning and attitudes from a Bayesian Network of log file data
A student's goals and attitudes while interacting with a tutor are typically unseen and unknowable. However their outward behavior (e.g. problem-solving time, mistakes and hel...
Ivon Arroyo, Beverly Park Woolf
186
Voted
ICDE
2008
IEEE
165views Database» more  ICDE 2008»
16 years 1 months ago
Online Failure Forecast for Fault-Tolerant Data Stream Processing
In this paper, we present a new online failure forecast system to achieve predictive failure management for fault-tolerant data stream processing. Different from previous reactive ...
Xiaohui Gu, Spiros Papadimitriou, Philip S. Yu, Sh...