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...time of exactly <math>n</math>. Consequently, every [[Decision tree model|decision tree algorithm]] that represents the function has, at worst case, a running ...for the same function has smaller complexity. For an evasive function, any decision tree must have at least one input that leads to a path in the tree along wh ...

...logarithm of the original input size. This is small enough for an optimal decision tree to be constructed by a [[brute-force search]], and then used to solve ...s not apply to algebraic decision trees. If there exist algebraic decision trees for the problem with depth <math>o(n^{4/3})</math>, they could be used in t ...

...es or as an extension of [[ω-automata|infinite-word automata]] to infinite trees. ...sely connected and it allows decision problems in logic to be reduced into decision problems for automata. ...

...nd examine decision makers’ concepts, as well as a tool for refining their decision knowledge and facilitating knowledge sharing in a scalable manner. ...=1970}}</ref> Following the de facto international standard (CSA, 1970), a decision table contains three building blocks: the conditions, the actions (or decis ...

A '''binary decision''' is a choice between two alternatives, for instance between taking some s *[[Decision tree]]s and [[binary decision diagram]]s, representations for sequences of binary decisions.<ref>{{citati ...

{{Short description|Decision tree training concept}} ...journal |last=Quinlan |first=J. R. |date=1986 |title=Induction of decision trees |journal=Machine Learning |volume=1 |pages=81–106 |doi=10.1007/BF00116251 | ...

[[File:Decision_tree_model.png|thumb|right|Decision Tree Model]] ...del of computation]] in which an [[algorithm]] can be considered to be a [[decision tree]], i.e. a sequence of ''queries'' or ''tests'' that are done adaptivel ...

{{harvtxt|Hu|1974}} writes that the problem of constructing these trees was proposed by Francesco Maffioli.{{r|hu}} ...t on the number of vertices of the input graph, and by searching among all trees with <math>k</math> internal nodes.{{r|wlbcrt}} ...

...mulating many possible future possibilities and using the results to guide decision-making. ...earch iteratively builds a search tree T representing the state space of a decision problem. Each node s in T represents a state, with child nodes representing ...

...e |last2=Ögren |first2=Petter |year=2018 |arxiv=1709.00084 |title=Behavior Trees in Robotics and AI: An Introduction |publisher=CRC Press |doi=10.1201/97804 ...Baumgarten |first2=R. |last3=Colton |first3=S. |chapter=Evolving Behaviour Trees for the Commercial Game DEFCON |series=Lecture Notes in Computer Science |v ...

..."Hard" classification can then be done using the [[Bayes estimator|optimal decision rule]]<ref name="bishop">{{cite book |first=Christopher M. |last=Bishop |ye ...ast2=Elkan |title=Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers |url=http://cseweb.ucsd.edu/~elkan/calibrate ...

...ategy requires the base classifiers to produce a real-valued score for its decision (see also [[scoring rule]]), rather than just a class label; discrete class ...al algorithms have been developed based on [[neural networks]], [[decision trees]], [[k-nearest neighbors]], [[naive Bayes]], [[support vector machines]] an ...

...l by Gallager was indeed optimal by a reduction from a generalized noisy [[decision tree]] and produced a <math>\Omega(n\log(n))</math> lower bound on the dept ...7| title = Time–space trade-off lower bounds for randomized computation of decision problems| journal = Journal of the ACM| volume = 50| issue = 2| pages = 154 ...

}}</ref> and Clinical Decision Support Systems <ref>{{cite journal| last1=Parodi|first1=S.| last2=Filibert ...e studied phenomenon. On the other hand, [[decision tree learning|decision trees]] were able to describe the phenomenon but often lacked accuracy. Switching ...

...other [[Boosting (machine learning)|boosting]] methods, a gradient-boosted trees model is built in stages, but it generalizes the other methods by allowing Gradient boosting is typically used with [[Decision tree learning|decision trees]] (especially [[Classification and regression tree|CARTs]]) of a fixed size ...

...sification or regression techniques, such as methods using a probabilistic decision tree, a neural network or a probabilistic support-vector machine. Hence, fo ...able and <math>\mathbf{X} - X_i</math> are the input variables. To learn a decision tree structure for <math>X_i</math>, the search algorithm begins with a sin ...

...al and self-explained, but following the paths of hundreds or thousands of trees is much harder. * Clever penalization of trees ...

...finite automaton|deterministic finite automata]], decision-lists, decision-trees, and other geometrically-defined concept classes. ...L. (1987). ''[http://people.csail.mit.edu/rivest/pubs/Riv87b.pdf Learning decision lists. Machine learning]'', 2(3), 229-246.</ref> ...

...rnal |title=Decidability of second-order theories and automata on infinite trees |last=Rabin |first=Michael |journal=Transactions of the American Mathematic * Arbitrary subsets of {0,1}^* are sometimes identified with trees, specifically as a {0,1}-labeled tree {0,1}^*; {0,1}^* ...

...th>b</math> is the proportionate loss in <math>\phi</math> from a negative decision. The barely-justified cost is thus inversely proportional to the residual r ...t breed. This is consistent with the bet hedging hypothesis, and indicates decision making on a large temporal scale (as spotted turtles may live for 65 to 110 ...