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» Evaluating algorithms that learn from data streams
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JMLR
2010
103views more  JMLR 2010»
14 years 10 months ago
Learning Nonlinear Dynamic Models from Non-sequenced Data
Virtually all methods of learning dynamic systems from data start from the same basic assumption: the learning algorithm will be given a sequence of data generated from the dynami...
Tzu-Kuo Huang, Le Song, Jeff Schneider
DAWAK
2006
Springer
15 years 7 months ago
Learning Classifiers from Distributed, Ontology-Extended Data Sources
Abstract. There is an urgent need for sound approaches to integrative and collaborative analysis of large, autonomous (and hence, inevitably semantically heterogeneous) data source...
Doina Caragea, Jun Zhang 0002, Jyotishman Pathak, ...
134
Voted
KDD
2007
ACM
192views Data Mining» more  KDD 2007»
16 years 4 months ago
Active exploration for learning rankings from clickthrough data
We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. ...
Filip Radlinski, Thorsten Joachims
KDD
1999
ACM
104views Data Mining» more  KDD 1999»
15 years 8 months ago
Learning Rules from Distributed Data
In this paper a concern about the accuracy (as a function of parallelism) of a certain class of distributed learning algorithms is raised, and one proposed improvement is illustrat...
Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowye...
106
Voted
KDD
2005
ACM
80views Data Mining» more  KDD 2005»
16 years 4 months ago
Wavelet synopsis for data streams: minimizing non-euclidean error
We consider the wavelet synopsis construction problem for data streams where given n numbers we wish to estimate the data by constructing a synopsis, whose size, say B is much sma...
Sudipto Guha, Boulos Harb