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KDD
1999
ACM
199views Data Mining» more  KDD 1999»
13 years 9 months ago
The Application of AdaBoost for Distributed, Scalable and On-Line Learning
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang
SAC
2006
ACM
13 years 11 months ago
Discretization from data streams: applications to histograms and data mining
Abstract. In this paper we propose a new method to perform incremental discretization. The basic idea is to perform the task in two layers. The first layer receives the sequence o...
João Gama, Carlos Pinto
KDD
2010
ACM
218views Data Mining» more  KDD 2010»
13 years 9 months ago
Online multiscale dynamic topic models
We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, so...
Tomoharu Iwata, Takeshi Yamada, Yasushi Sakurai, N...
ICDM
2009
IEEE
148views Data Mining» more  ICDM 2009»
14 years 5 days ago
Online System Problem Detection by Mining Patterns of Console Logs
Abstract—We describe a novel application of using data mining and statistical learning methods to automatically monitor and detect abnormal execution traces from console logs in ...
Wei Xu, Ling Huang, Armando Fox, David Patterson, ...
WWW
2008
ACM
14 years 6 months ago
Floatcascade learning for fast imbalanced web mining
This paper is concerned with the problem of Imbalanced Classification (IC) in web mining, which often arises on the web due to the "Matthew Effect". As web IC applicatio...
Xiaoxun Zhang, Xueying Wang, Honglei Guo, Zhili Gu...