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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
51#
發(fā)表于 2025-3-30 08:20:22 | 只看該作者
52#
發(fā)表于 2025-3-30 14:18:21 | 只看該作者
53#
發(fā)表于 2025-3-30 19:16:52 | 只看該作者
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發(fā)表于 2025-3-30 21:12:11 | 只看該作者
A Hybrid Architecture Based on CNN for Image Semantic Annotationthe performance of image annotation, automatic image annotation has became an important research hotspots. In this paper, a hybrid approach is proposed to learn automatically semantic concepts of images, which is called Deep-CC. First we utilize the convolutional neural network for feature learning,
55#
發(fā)表于 2025-3-31 01:55:00 | 只看該作者
Convolutional Neural Networks Optimized by Logistic Regression Modelduces the application of two kinds of logistic regression classifier in the convolutional neural network. The first classifier is a logistic regression classifier, which is a classifier for two classification problems, but it can also be used for multi-classification problems. The second kind of cla
56#
發(fā)表于 2025-3-31 08:53:43 | 只看該作者
Event Detection with Convolutional Neural Networks for Forensic Investigationiques are not competent to extract information from digital artifacts collected for investigation. In this paper, we propose an improved framework based on a Convolutional neural network (CNN) to capture significant clues for event identification. The experiments show that our solution achieves exce
57#
發(fā)表于 2025-3-31 12:48:15 | 只看該作者
Boltzmann Machine and its Applications in Image Recognitionne. This paper built Weight uncertainty RBM model based on maximum likelihood estimation. And in the experimental section, this paper verified the effectiveness of the Weight uncertainty Deep Belief Network and the Weight uncertainty Deep Boltzmann Machine. In order to improve the images recognition
58#
發(fā)表于 2025-3-31 16:00:05 | 只看該作者
59#
發(fā)表于 2025-3-31 17:39:38 | 只看該作者
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