Topic
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.
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11 Aug 2005
TL;DR: A large number of the time-series forecasting models used in this study have been developed and improved over the past several decades and are suitable for use in the oil and gas industry.
Abstract: Time-series forecasting is an important research and application area. Much effort has been devoted over the past several decades to develop and improve the time-series forecasting models. This pap...
7 citations
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20 Oct 2007TL;DR: This model can alleviate the limitations of a single tree-structured model for human pose estimation by combining information provided across different tree models, and presents experimental results showing the improvement of the model over previous approaches on a very challenging dataset.
Abstract: Tree-structured models have been widely used for human pose estimation, in either 2D or 3D. While such models allow efficient learning and inference, they fail to capture additional dependencies between body parts, other than kinematic constraints. In this paper, we consider the use of multiple tree models, rather than a single tree model for human pose estimation. Our model can alleviate the limitations of a single tree-structured model by combining information provided across different tree models. The parameters of each individual tree model are trained via standard learning algorithms in a single tree-structured model. Different tree models are combined in a discriminative fashion by a boosting procedure. We present experimental results showing the improvement of our model over previous approaches on a very challenging dataset.
7 citations
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TL;DR: The proposed stochastic multiple imputation algorithm is advantageous for identifying the true underlying covariate structure when complex data and larger percentages of missing covariate observations are present and is competitive with other current methods with respect to prediction accuracy.
Abstract: Missing covariate data present a challenge to tree-structured methodology due to the fact that a single tree model, as opposed to an estimated parameter value, may be desired for use in a clinical setting. To address this problem, we suggest a multiple imputation algorithm that adds draws of stochastic error to a tree-based single imputation method presented by Conversano and Siciliano (Technical Report, University of Naples, 2003). Unlike previously proposed techniques for accommodating missing covariate data in tree-structured analyses, our methodology allows the modeling of complex and nonlinear covariate structures while still resulting in a single tree model. We perform a simulation study to evaluate our stochastic multiple imputation algorithm when covariate data are missing at random and compare it to other currently used methods. Our algorithm is advantageous for identifying the true underlying covariate structure when complex data and larger percentages of missing covariate observations are present. It is competitive with other current methods with respect to prediction accuracy. To illustrate our algorithm, we create a tree-structured survival model for predicting time to treatment response in older, depressed adults.
7 citations
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TL;DR: It is shown that, forn×n matrices whose entries are elements of a finite field of sizep, the communication complexity of this problem is Θ(n2 logp), which implies tight bounds for several other problems liked determining the rank and computing the determinant.
Abstract: We investigate the communication complexity of singularity testing in a finite field, where the problem is to determine whether a given square matrixM is singular. We show that, forn×n matrices whose entries are elements of a finite field of sizep, the communication complexity of this problem is Θ(n2 logp). Our results imply tight bounds for several other problems likedetermining the rank andcomputing the determinant.
7 citations