The existing monotonic decision tree algorithms are based on a linearly ordered constraint that certain attributes are monotonously consistent with the decision, which could be called monotonic attributes, whereas oth...
The existing monotonic decision tree algorithms are based on a linearly ordered constraint that certain attributes are monotonously consistent with the decision, which could be called monotonic attributes, whereas others, called non-monotonic attributes. In practice, monotonic and non-monotonic attributes coexist in most classification tasks, and some attribute values are even evaluated as interval numbers. In this paper, we proposed a fuzzy rank-inconsistent rate based on probability degree to judge the monotonicity of interval numbers. Furthermore, we devised a hybrid model composed of monotonic and non-monotonic attributes to construct a mixed monotone decision tree for interval-valued data. Experiments on artificial and real-world data sets show that the proposed hybrid model is effective.
Given a convex set Ω of R n , we consider the shape optimization problem of finding a convex subset ω ⊂ Ω , of a given measure, minimizing the p -distance functional J p ( ω ) ≔ ∫ S n − 1 | h Ω − h ω | p d H n ...
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Given a convex set Ω of R n , we consider the shape optimization problem of finding a convex subset ω ⊂ Ω , of a given measure, minimizing the p -distance functional J p ( ω ) ≔ ∫ S n − 1 | h Ω − h ω | p d H n − 1 1 p , where 1 ≤ p < ∞ and h ω and h Ω are the support functions of ω and the fixed container Ω , respectively. We prove the existence of solutions and show that this minimization problem Γ -converges, when p tends to + ∞ , towards the problem of finding a convex subset ω ⊂ Ω , of a given measure, minimizing the Hausdorff distance to the convex Ω . In the planar case, we show that the free parts of the boundary of the optimal shapes, i.e., those that are in the interior of Ω , are given by polygonal lines. Still in the 2D setting, from a computational perspective, the classical method based on optimizing Fourier coefficients of support functions is not efficient, as it is unable to efficiently capture the presence of segments on the boundary of optimal shapes. We subsequently propose a method combining Fourier analysis and a numerical scheme recently introduced in Bogosel (2023), allowing to obtain accurate results, as demonstrated through numerical experiments.
Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, ...
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In practice, the parameters of control policies are often tuned manually. This is time-consuming and frustrating. Reinforcement learning is a promising alternative that aims to automate this process, yet often require...
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Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number o...
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Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].
One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reach a certain goal state. Traditionally...
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— In practice, the parameters of control policies are often tuned manually. This is time-consuming and frustrating. Reinforcement learning is a promising alternative that aims to automate this process, yet often requ...
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In this paper, we describe quantitative graph theory and argue it is a new graph-theoretical branch in network science, however, with significant different features compared to classical graph theory. The main goal of...
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One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and it...
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ISBN:
(纸本)9781467356404
One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection by controlling the point of view of a mobile depth camera. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. The sensor then plans a sequence of viewpoints, which balances the amount of energy used to move with the chance of identifying the correct hypothesis. We formulate an active M-ary hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate POMDP algorithm. The validity of our approach is verified through simulation and experiments with real scenes captured by a kinect sensor. The results suggest a significant improvement over static object detection.
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