Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

Properties and Application of Nondeterministic Quantum Query Algorithms.

Abstract: Many quantum algorithms can be analyzed in a query model to compute Boolean functions where input is given by a black box. As in the classical version of decision trees, different kinds of quantum query algorithms are possible: exact, with bounded error and even nondeterministic. In this paper, we study the latter class of algorithms. We introduce a new notion in addition to already studied nondeterministic algorithms and introduce dual nondeterministic quantum query algorithms. We examine properties of such algorithms and prove relations with exact and nondeterministic quantum query algorithm complexity. As a result and as an example of the application of discovered properties, we demonstrate a gap of n vs. 2 between classical deterministic and dual nondeterministic quantum query complexity for a specific Boolean function. Finally, we show an approach how to construct examples where quantum nondeterministic complexity of an algorithm is O (1), however classical deterministic algorithm for the same function would require O ( n ) queries.

Multi-party joint risk recognition method and device

Abstract: The embodiment of the invention provides a multi-party joint risk recognition method and device. The method comprises the steps that a first station obtains a first sub-model of a safety tree model jointly trained with a second station; the safety tree model is also provided with a second sub-model deployed at a second site; obtaining a third sub-model obtained according to a tree structure corresponding to a preset risk recognition strategy; the tree structure is further provided with a fourth sub-model deployed at the second site; when it is determined that a preset risk recognition condition is satisfied, obtaining first feature data of each feature in a first feature set of the target user; inputting the first feature data into a first sub-model and a third sub-model to respectively obtain a first prediction score and a third prediction score; and providing the first prediction score and the third prediction score in a multi-party safety calculation mode, and comprehensively determining whether the target user has the first risk or not by combining the first prediction score and the third prediction score with the second prediction score and the fourth prediction score providedby the second station. The privacy information of the user can be prevented from being leaked.

Time-Series Forecasting Based on Neural Tree Model

Abstract: A new neural tree for modeling the time-series forecasting is proposed in the paper.Grammar guided genetic programming was used to optimize the structure of neural tree for modeling the time-series forecasting in this paper,and we compared the performance with an artificial(network) model for time-(series) forecasting.The result shows that our model has a higher reliability than the artificial network model.

Multi-branch tree model of bend for curve and its application in map generalization

Abstract: This paper researched the relation between bend multi-branch tree of curve and multi-scale map representation based on bends as a basic structure unit of curve,and proposed the model of multi-branch tree of bend for curve shape,which was based on bend binary tree,whose defects had been pointed out.Furthermore,it brought up a generalization strategy for curve multi-branch tree of bend.The experimental results that the model is used in contour simplification show the algorithm proves very effective in the aspects of holding the shape of line feature,geographic-feature consistency.