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» Inducing Decision Trees via Concept Lattices
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AAAI
2006
13 years 11 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
14 years 4 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 4 months 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 10 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 11 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