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» Inducing Decision Trees via Concept Lattices
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AAAI
2006
13 years 6 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
GLOBECOM
2007
IEEE
13 years 11 months ago
Reduced Complexity Sphere Decoding for Square QAM via a New Lattice Representation
— Sphere decoding (SD) is a low complexity maximum likelihood (ML) detection algorithm, which has been adapted for different linear channels in digital communications. The comple...
Luay Azzam, Ender Ayanoglu
SAC
2010
ACM
14 years 10 days ago
Towards the induction of terminological decision trees
A concept learning framework for terminological representations is introduced. It is grounded on a method for inducing logic decision trees as an adaptation of the classic tree in...
Nicola Fanizzi, Claudia d'Amato, Floriana Esposito
ICML
2004
IEEE
14 years 6 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 6 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