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.

A Light-weight Approach for Automatically Reconstructing Large-scale Trees

Abstract: Different from previous tree modeling approaches, our method is based on the idea of making tree reconstruction as quick as possible and simplifying the representation of final results while keeping the tree model visually acceptable. Each tree is represented by Billboard model. We first get the shape mask of a tree by projecting LiDAR point cloud onto 2D camera plane. Then we use the shape fitting method to obtain the corresponding rotation axes and bounding boxes for the main trunk and tree crown. We get the corresponding texture and correct the misalignment artifacts by texture completion. Finally, we rotate each textured polygon around the rotation axis to a certain degree. We demonstrate the effectiveness of our system with some LiDAR data sets and compare our tree modeling scheme with other state-of-the-art reconstruction algorithms to show its advantages in terms of speed and memory footprint.

A Tree Graph Model Based on Mobile Environment

Abstract: Both of mobile multimedia and mobile Internet are the important development directions of the mobile service. However, it would take on great cost by using the high data transmission rate of wireless multimedia communication service. Under the premise of not increasing the investment in hardware, the personalized service could be applied to the mobile service to not only reducing wireless multimedia communication cost but also keeping the quality of mobile service for users. This paper presents a kind of model on tree graph that works on server-side. And it integrates individual requirements of users in mobile terminal with recommended information resources on the Internet in order to organize multicast push. This paper explains a method of a tree model in theory, brings forward a series of correlative definitions and formulae of founding the model, and designs an Algorithm on the Tree Graph Node’s Establishment and Updating. Finally, it uses an implement of well-ordered to prove strictly the algorithm’s determinacy, and then, analyzes time complexity of the algorithm. On the theory, the algorithm is proved of the traits of determinacy, effectiveness and low time complexity.

Algebraic tests of general Gaussian latent tree models.

Abstract: We consider general Gaussian latent tree models in which the observed variables are not restricted to be leaves of the tree. Extending related recent work, we give a full semi-algebraic description of the set of covariance matrices of any such model. In other words, we find polynomial constraints that characterize when a matrix is the covariance matrix of a distribution in a given latent tree model. However, leveraging these constraints to test a given such model is often complicated by the number of constraints being large and by singularities of individual polynomials, which may invalidate standard approximations to relevant probability distributions. Illustrating with the star tree, we propose a new testing methodology that circumvents singularity issues by trading off some statistical estimation efficiency and handles cases with many constraints through recent advances on Gaussian approximation for maxima of sums of high-dimensional random vectors. Our test avoids the need to maximize the possibly multimodal likelihood function of such models and is applicable to models with larger number of variables. These points are illustrated in numerical experiments.

Constructing additive trees monotonic in selected sets of variables

Abstract: A system and method for generating monotonicity constraints and integrating the monotonicity constraints with an additive tree model includes receiving the additive tree model trained on a dataset, receiving a selection of a set of subsets of variables on which to impose monotonicity of partial dependence functions, generating a set of monotonicity constraints for the partial dependence functions in the selected set of subsets of variables based on the dataset and a set of parameters of the additive tree model, receiving a selection of an objective function, and optimizing the objective function subject to the set of monotonicity constraints.