卑贱
发表于 2025-3-21 19:38:14
书目名称Intelligent Computing Methodologies影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0469459<br><br> <br><br>书目名称Intelligent Computing Methodologies影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0469459<br><br> <br><br>书目名称Intelligent Computing Methodologies网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0469459<br><br> <br><br>书目名称Intelligent Computing Methodologies网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0469459<br><br> <br><br>书目名称Intelligent Computing Methodologies被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0469459<br><br> <br><br>书目名称Intelligent Computing Methodologies被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0469459<br><br> <br><br>书目名称Intelligent Computing Methodologies年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0469459<br><br> <br><br>书目名称Intelligent Computing Methodologies年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0469459<br><br> <br><br>书目名称Intelligent Computing Methodologies读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0469459<br><br> <br><br>书目名称Intelligent Computing Methodologies读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0469459<br><br> <br><br>
cochlea
发表于 2025-3-21 21:29:43
Solving Three-Objective Flow Shop Problem with Fast Hypervolume-Based Local Search Algorithmng computation of hypervolume contribution. In the algorithm, we define an approximate hypervolume contribution indicator as the selection mechanism and apply this indicator to an iterated local search. We carry out a range of experiments on three-objective flow shop problem. Experimental results in
cumulative
发表于 2025-3-22 01:54:19
A Learning Automata-Based Singular Value Decomposition and Its Application in Recommendation Systemd at the same time bringing large profits to e-commerce companies. Till now many different recommendation algorithm have been proposed and achieved good effect. In the context Netflix Prize in 2006, Simon Funk proposed a matrix factorization-based recommendation algorithm named Funk-SVD, which cause
残暴
发表于 2025-3-22 06:46:06
A Novel 2-Stage Combining Classifier Model with Stacking and Genetic Algorithm Based Feature Selectia is first generated by stacking on the original data (called Level0 data) with base classifiers. Level1data is then classified by a second classifier (denoted by C) with feature selection using GA. The advantage of applying GA on Level1 data is that it has lower dimension and is more uniformity tha
conscribe
发表于 2025-3-22 11:44:06
Improved Bayesian Network Structure Learning with Node Ordering via K2 Algorithmm can reduce search space effectively, improve learning efficiency, but it requires the initial node ordering as input, which is very limited by the absence of the priori information. On the other hand, search process of K2 algorithm uses a greedy search strategy and solutions are easy to fall into
agglomerate
发表于 2025-3-22 16:32:55
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slow-wave-sleep
发表于 2025-3-22 20:09:05
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Offbeat
发表于 2025-3-23 00:13:38
Comparison of EM-Based Algorithms and Image Segmentation Evaluatione model depends on unobserved latent variables. The idea behind the EM algorithm is intuitive and natural, which makes it applicable to a variety of problems. However, the EM algorithm does not guarantee convergence to the global maximum when there are multiple local maxima. In this paper, a random
N防腐剂
发表于 2025-3-23 04:28:42
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生命
发表于 2025-3-23 06:26:31
The Equivalence Relationship between Kernel Functions Based on SVM and Four-Layer Functional Networkl network and kernel functions based SVM, and the equivalent relationship between functional networks with SVM is demonstrated. This result provides us a very useful guideline when we perform theoretical research and applications on design SVM, functional network systems.