A Lower Bound for Randomized Algebraic Decision Trees.
Dima GrigorievMarek KarpinskiFriedhelm Meyer auf der HeideRoman SmolenskyPublished in: Comput. Complex. (1997)
Keyphrases
- lower bound
- decision trees
- randomized algorithms
- upper bound
- decision forest
- randomized algorithm
- branch and bound algorithm
- branch and bound
- lower and upper bounds
- naive bayes
- objective function
- worst case
- decision tree algorithm
- optimal solution
- machine learning algorithms
- random forest
- np hard
- decision tree induction
- machine learning
- predictive accuracy
- lower bounding
- decision rules
- data sets
- training data
- linear programming relaxation
- feature construction
- decision tree learning
- constructive induction
- sufficiently accurate
- induction algorithms
- upper and lower bounds
- sample complexity
- rule sets
- lagrangian relaxation
- nearest neighbour
- rule induction
- random forests
- classification models
- ensemble methods
- data mining methods
- classification rules
- polynomial approximation
- linear programming