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Titlebook: Intelligent Information Processing VIII; 9th IFIP TC 12 Inter Zhongzhi Shi,Sunil Vadera,Gang Li Conference proceedings 2016 IFIP Internatio

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樓主: ominous
21#
發(fā)表于 2025-3-25 03:42:16 | 只看該作者
An Attribute-Value Block Based Method of Acquiring Minimum Rule Sets: A Granulation Method to Constr are proposed, which, together with related attribute reduction algorithm, constitute an effective granulation method to acquire minimum rule sets, which is a kind classifier and can be used for class prediction. At last, related experiments are conducted to demonstrate that the proposed methods are effective and feasible.
22#
發(fā)表于 2025-3-25 11:16:37 | 只看該作者
23#
發(fā)表于 2025-3-25 15:40:31 | 只看該作者
Convolutional Neural Networks Optimized by Logistic Regression Modelssifier is a multi-classification logistic regression classifier, also known as softmax regression classifier. Two kinds of classifiers have achieved good results in MNIST handwritten digit recognition.
24#
發(fā)表于 2025-3-25 18:09:19 | 只看該作者
25#
發(fā)表于 2025-3-25 21:01:02 | 只看該作者
Application of Manifold Learning to Machinery Fault Diagnosisding is used to extract the essential nonlinear structure of feature space. Afterwards, the fault diagnosis is implemented with spectral clustering and support vector machine. The experiment demonstrates that the approach can effectively diagnose the fault of Machinery.
26#
發(fā)表于 2025-3-26 03:49:15 | 只看該作者
Boltzmann Machine and its Applications in Image Recognitionn Machine (wssDBM). The experiments showed that, the Weight uncertainty RBM, Weight uncertainty DBN and Weight uncertainty DBM were effective compared with the dropout method. At last, we validate the effectiveness of wssDBM in experimental section.
27#
發(fā)表于 2025-3-26 07:14:09 | 只看該作者
28#
發(fā)表于 2025-3-26 09:37:18 | 只看該作者
Anomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal Featuresy detection in an unsupervised manner. We investigate three different approaches to extracting and representing spatio-temporal features, and we demonstrate the effectiveness of our proposed feature representation on a standard benchmark dataset and a real-life video surveillance environment.
29#
發(fā)表于 2025-3-26 14:57:08 | 只看該作者
30#
發(fā)表于 2025-3-26 17:35:48 | 只看該作者
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