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Titlebook: Clusters, Orders, and Trees: Methods and Applications; In Honor of Boris Mi Fuad Aleskerov,Boris Goldengorin,Panos M. Pardalos Book 2014 Sp

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11#
發(fā)表于 2025-3-23 11:29:37 | 只看該作者
12#
發(fā)表于 2025-3-23 15:55:12 | 只看該作者
Sch?dliche Nebenwirkungen von Arzneimitteln the breakpoints of the piecewise linear function . on [0; +.). In particular we show that when . is convex and increasing, . is concave, increasing and strictly positive, . and ? . are supermodular and either . or . has a monotonicity property, then the 0–1 fractional programming problem can be sol
13#
發(fā)表于 2025-3-23 20:05:58 | 只看該作者
14#
發(fā)表于 2025-3-24 01:03:18 | 只看該作者
Sch?dliche Nebenwirkungen von Arzneimittelnns satisfying conditions meant to please the entire user set as much as possible. A?strategy-proof location function is one for which it never benefits a user to report a suboptimal preferred location. We introduce four versions of strategy-proof and prove some preliminary results focusing on two we
15#
發(fā)表于 2025-3-24 04:41:41 | 只看該作者
https://doi.org/10.1007/978-3-7091-7860-7hes. The first step of such an analysis is to describe the considered financial market via the correlation matrix of stocks prices over a certain period of time. The second step is to build a graph in which vertices represent stocks and edge weights represent correlation coefficients between the cor
16#
發(fā)表于 2025-3-24 08:40:01 | 只看該作者
https://doi.org/10.1007/978-3-7091-7860-73D point clouds that are collected by laser range finders and consumer devices like Microsoft Kinect. We present a novel procedure for 3D image registration that is a fundamental step in 3D objects reconstruction. This procedure reduces the task complexity by extracting small subset of potential mat
17#
發(fā)表于 2025-3-24 11:45:21 | 只看該作者
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發(fā)表于 2025-3-24 18:38:49 | 只看該作者
19#
發(fā)表于 2025-3-24 22:33:17 | 只看該作者
Sch?dliche Nebenwirkungen von Arzneimittelnsupervised learning), and semi-supervised learning. These problems differ from each other based on the ratio of labeled and unlabeled objects in a training dataset. In the classification problem all the objects are labeled, and in the clustering problem all the objects are unlabeled. Semi-supervised
20#
發(fā)表于 2025-3-25 01:42:23 | 只看該作者
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