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Titlebook: Intelligent Computing Theories and Methodologies; 11th International C De-Shuang Huang,Vitoantonio Bevilacqua,Prashan Pre Conference procee

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樓主: Carter
21#
發(fā)表于 2025-3-25 13:49:26 | 只看該作者
22#
發(fā)表于 2025-3-25 16:21:41 | 只看該作者
23#
發(fā)表于 2025-3-25 21:41:14 | 只看該作者
The Chaotic Measurement Matrix for Compressed Sensing,is paper, we present a simple and efficient measurement matrix named Incoherence Rotated Chaotic (IRC) matrix. We take advantage of the well pseudorandom of chaotic sequence, introduce the concept of the incoherence factor and rotation, and adopt QR decomposition to obtain the IRC measurement matrix
24#
發(fā)表于 2025-3-26 02:42:50 | 只看該作者
25#
發(fā)表于 2025-3-26 06:49:01 | 只看該作者
How to Detect Communities in Large Networks,sed, such as graph partitioning, hierarchical clustering, partitional clustering. Due to the high computational complexity of those algorithms, it is impossible to apply those algorithms to large networks. In order to solve the problem, Blondel introduced a new greedy approach named lovian to apply
26#
發(fā)表于 2025-3-26 10:48:54 | 只看該作者
Self-adaptive Percolation Behavior Water Cycle Algorithm,towards the sea in the real world. In this paper, a new self-adaptive water cycle algorithm with percolation behavior is proposed. The percolation behavior is introduced to accelerate the convergence speed of proposed algorithm. At the same time, a self-adaptive rainfall process can generate the new
27#
發(fā)表于 2025-3-26 14:51:44 | 只看該作者
An Online Supervised Learning Algorithm Based on Nonlinear Spike Train Kernels,es have been achieved in developing online learning approaches for spikingneural networks. This paper presents an online supervised learning algorithm based on nonlinear spike train kernels to process the spatiotemporal information, which is more biological interpretability. The main idea adopts onl
28#
發(fā)表于 2025-3-26 17:12:35 | 只看該作者
29#
發(fā)表于 2025-3-26 21:34:19 | 只看該作者
A Water Wave Optimization Algorithm with Variable Population Size and Comprehensive Learning,eaking. In this paper we present a variation of WWO, named VC-WWO, which adopts a variable population size to accelerate the search process, and develops a comprehensive learning mechanism in the refraction operator to make stationary waves learn from more exemplars to increase the solution diversit
30#
發(fā)表于 2025-3-27 03:50:16 | 只看該作者
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