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KDD
2009
ACM
180views Data Mining» more  KDD 2009»
16 years 4 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
DASFAA
2005
IEEE
157views Database» more  DASFAA 2005»
15 years 10 months ago
Adaptively Detecting Aggregation Bursts in Data Streams
Finding bursts in data streams is attracting much attention in research community due to its broad applications. Existing burst detection methods suffer the problems that 1) the p...
Aoying Zhou, Shouke Qin, Weining Qian
KDD
2005
ACM
143views Data Mining» more  KDD 2005»
16 years 4 months ago
SVM selective sampling for ranking with application to data retrieval
Learning ranking (or preference) functions has been a major issue in the machine learning community and has produced many applications in information retrieval. SVMs (Support Vect...
Hwanjo Yu
TKDE
2008
153views more  TKDE 2008»
15 years 4 months ago
Discovering Frequent Agreement Subtrees from Phylogenetic Data
We study a new data mining problem concerning the discovery of frequent agreement subtrees (FASTs) from a set of phylogenetic trees. A phylogenetic tree, or phylogeny, is an unorde...
Sen Zhang, Jason Tsong-Li Wang
SDM
2009
SIAM
129views Data Mining» more  SDM 2009»
16 years 1 months ago
Scalable Distributed Change Detection from Astronomy Data Streams Using Local, Asynchronous Eigen Monitoring Algorithms.
This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines such as the Larg...
Kamalika Das, Kanishka Bhaduri, Sugandha Arora, We...