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ICML
2004
IEEE
14 years 7 months ago
Lookahead-based algorithms for anytime induction of decision trees
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
Saher Esmeir, Shaul Markovitch
AAAI
2006
13 years 8 months ago
Anytime Induction of Decision Trees: An Iterative Improvement Approach
Most existing decision tree inducers are very fast due to their greedy approach. In many real-life applications, however, we are willing to allocate more time to get better decisi...
Saher Esmeir, Shaul Markovitch
AAAI
2006
13 years 8 months ago
When a Decision Tree Learner Has Plenty of Time
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
Saher Esmeir, Shaul Markovitch
JMLR
2002
102views more  JMLR 2002»
13 years 6 months ago
Efficient Algorithms for Decision Tree Cross-validation
Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward...
Hendrik Blockeel, Jan Struyf
JAIR
2008
120views more  JAIR 2008»
13 years 7 months ago
Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach
Machine learning techniques are gaining prevalence in the production of a wide range of classifiers for complex real-world applications with nonuniform testing and misclassificati...
Saher Esmeir, Shaul Markovitch