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ICDM
2007
IEEE
131views Data Mining» more  ICDM 2007»
15 years 5 months ago
Predicting and Optimizing Classifier Utility with the Power Law
When data collection is costly and/or takes a significant amount of time, an early prediction of the classifier performance is extremely important for the design of the data minin...
Mark Last
AUSAI
2006
Springer
15 years 5 months ago
Voting Massive Collections of Bayesian Network Classifiers for Data Streams
Abstract. We present a new method for voting exponential (in the number of attributes) size sets of Bayesian classifiers in polynomial time with polynomial memory requirements. Tra...
Remco R. Bouckaert
108
Voted
DIS
2004
Springer
15 years 5 months ago
Maximum a Posteriori Tree Augmented Naive Bayes Classifiers
Bayesian classifiers such as Naive Bayes or Tree Augmented Naive Bayes (TAN) have shown excellent performance given their simplicity and heavy underlying independence assumptions....
Jesús Cerquides, Ramon López de M&aa...
110
Voted
AUSAI
2003
Springer
15 years 5 months ago
On Why Discretization Works for Naive-Bayes Classifiers
We investigate why discretization is effective in naive-Bayes learning. We prove a theorem that identifies particular conditions under which discretization will result in naiveBay...
Ying Yang, Geoffrey I. Webb
ACL
2006
15 years 3 months ago
Discriminative Classifiers for Deterministic Dependency Parsing
Deterministic parsing guided by treebankinduced classifiers has emerged as a simple and efficient alternative to more complex models for data-driven parsing. We present a systemat...
Johan Hall, Joakim Nivre, Jens Nilsson