有作用
发表于 2025-3-21 17:28:45
书目名称Artificial Neural Networks in Pattern Recognition影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0162684<br><br> <br><br>书目名称Artificial Neural Networks in Pattern Recognition影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0162684<br><br> <br><br>书目名称Artificial Neural Networks in Pattern Recognition网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0162684<br><br> <br><br>书目名称Artificial Neural Networks in Pattern Recognition网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0162684<br><br> <br><br>书目名称Artificial Neural Networks in Pattern Recognition被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0162684<br><br> <br><br>书目名称Artificial Neural Networks in Pattern Recognition被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0162684<br><br> <br><br>书目名称Artificial Neural Networks in Pattern Recognition年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0162684<br><br> <br><br>书目名称Artificial Neural Networks in Pattern Recognition年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0162684<br><br> <br><br>书目名称Artificial Neural Networks in Pattern Recognition读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0162684<br><br> <br><br>书目名称Artificial Neural Networks in Pattern Recognition读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0162684<br><br> <br><br>
易受刺激
发表于 2025-3-21 21:11:49
Kernel Robust Soft Learning Vector Quantizationability of the model complexity. Recent prototype-based models such as robust soft learning vector quantization (RSLVQ) have the benefit of a solid mathematical foundation of the learning rule and decision boundaries in terms of probabilistic models and corresponding likelihood optimization. In its
Ingredient
发表于 2025-3-22 01:26:50
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Intractable
发表于 2025-3-22 06:07:42
Representative Prototype Sets for Data Characterization and Classificationiers do not allow for drawing conclusions on the structure and quality of the underlying training data. By keeping the classifier model simple, an intuitive interpretation of the model and the corresponding training data is possible. A lack of accuracy of such simple models can be compensated by acc
AWL
发表于 2025-3-22 11:12:41
Feature Selection by Block Addition and Block Deletionn this paper, we extend these methods to feature selection. To avoid random tie breaking for a small sample size problem with a large number of features, we introduce the weighted sum of the recognition error rate and the average of margin errors as the feature selection and feature ranking criteria
Genteel
发表于 2025-3-22 14:28:49
Gradient Algorithms for Exploration/Exploitation Trade-Offs: Global and Local Variantsces. Global and local variants are evaluated in discrete and continuous state spaces. The global variant is memory efficient in terms of requiring exploratory data only for starting states. In contrast, the local variant requires exploratory data for each state of the state space, but produces explo
调整校对
发表于 2025-3-22 19:32:00
Towards a Novel Probabilistic Graphical Model of Sequential Data: Fundamental Notions and a Solution Random Fields (MRFs) in terms of computational efficiency and modeling capabilities (namely, HRFs subsume BNs and MRFs). As in traditional graphical models, HRFs express a joint distribution over a fixed collection of random variables. This paper introduces the major definitions of a proper dynamic
poliosis
发表于 2025-3-22 23:09:32
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值得赞赏
发表于 2025-3-23 03:27:44
Statistical Recognition of a Set of Patterns Using Novel Probability Neural Networkir equivalence to the optimal Bayesian decision of classification task. However, it is known that the PNN’s conventional implementation is not optimal in statistical recognition of a set of patterns. In this article we present the novel modification of the PNN and prove that it is optimal in this ta
Transfusion
发表于 2025-3-23 06:09:59
On Graph-Associated Matrices and Their Eigenvalues for Optical Character Recognitionptical character recognition. The extracted eigenvalues were utilized as feature vectors for multi-class classification using support vector machines. Each graph-associated matrix contains a certain type of geometric/spacial information, which may be important for the classification process. Classif