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» Evaluating algorithms that learn from data streams
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SAC
2009
ACM
15 years 11 months ago
Combining statistics and semantics via ensemble model for document clustering
Incorporating background knowledge into data mining algorithms is an important but challenging problem. Current approaches in semi-supervised learning require explicit knowledge p...
Samah Jamal Fodeh, William F. Punch, Pang-Ning Tan
SIGMOD
2007
ACM
188views Database» more  SIGMOD 2007»
16 years 4 months ago
Keyword search on relational data streams
Increasing monitoring of transactions, environmental parameters, homeland security, RFID chips and interactions of online users rapidly establishes new data sources and applicatio...
Alexander Markowetz, Yin Yang, Dimitris Papadias
CORR
2008
Springer
216views Education» more  CORR 2008»
15 years 4 months ago
Building an interpretable fuzzy rule base from data using Orthogonal Least Squares Application to a depollution problem
In many fields where human understanding plays a crucial role, such as bioprocesses, the capacity of extracting knowledge from data is of critical importance. Within this framewor...
Sébastien Destercke, Serge Guillaume, Brigi...
UM
2007
Springer
15 years 10 months ago
Evaluating a Simulated Student Using Real Students Data for Training and Testing
: SimStudent is a machine-learning agent that learns cognitive skills by demonstration. It was originally developed as a building block of the Cognitive Tutor Authoring Tools (CTAT...
Noboru Matsuda, William W. Cohen, Jonathan Sewall,...
AI
2003
Springer
15 years 9 months ago
Explanation-Oriented Association Mining Using a Combination of Unsupervised and Supervised Learning Algorithms
We propose a new framework of explanation-oriented data mining by adding an explanation construction and evaluation phase to the data mining process. While traditional approaches c...
Yiyu Yao, Yan Zhao, R. Brien Maguire