基于流量的分类：特征歧视器及其评估数据集

流量分析特征属性
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"《基于流量的分类器使用探讨》是一篇经典的流量分析文章，发表于2005年，由Andrew Moore、Denis Zuev和Michael Crogan三位作者合作完成。该论文主要关注在流量分析领域中，如何设计和利用特征（或称为歧视器）进行对象分类。论文的焦点在于提供一组用于评估分类技术的数据集，这些数据集包含大量的TCP流量样本，每个样本（即对象）由一组应用中心类别（通过其他方法确定）和作为输入用于概率分类技术的特征组成。 文章的核心目标是为评估分类算法的性能提供标准化的数据资源，通过这些数据，研究人员可以测试和比较不同分类器的效能。每个对象（TCP流量流）的特点不仅限于其所属的应用类别，还涵盖了那些通过概率模型提取的额外特征，如包速率、包大小分布、时延特性等，这些特征对于理解网络行为和区分不同类型的流量至关重要。 此外，作者还详细描述了数据集的构建过程，包括如何收集和标记来自客户端与服务器之间的TCP数据流，以及如何确保手工地对这些数据进行分类，以便为机器学习模型提供高质量的训练样本。通过对这些特征的深入分析，论文旨在促进流量分类领域的研究进展，并推动算法的改进和优化。 《Discriminators for use in flow-based classification》这篇论文为流量分析研究者提供了一个重要的工具和资源，帮助他们开发出更准确、更具针对性的分类器，以应对日益复杂的网络环境中的流量管理与安全问题。"

A comparison of the Visual Aural Digit Span and the Bender Gestalt as discriminators of low achievement in the primary grades

2021-06-29 上传
A comparison of the Visual Aural Digit Span and the Bender Gestalt as discriminators of low achievement in the primary grades A COMPARISON OF THE VISUAL AURAL DIGIT SPAN AND THE BENDER GESTALT AS ...

Unsupervised-Attention-guided-Image-to-Image-Translation-master.zip

2019-12-15 上传
Motivated by the important role of attention in human perception, we tackle this limitation by introducing unsupervised attention mechanisms which are jointly adversarially trained with the generators...

NICE-GAN-pytorch:NICE-GAN的PyTorch正式实施

2021-03-12 上传
NICE-GAN —官方PyTorch实施 重用鉴别符进行编码：实现无监督的图像到图像的翻译 重用鉴别符进行编码：实现无监督的图像到图像的翻译 摘要无监督的图像到图像转换是计算机视觉中的中心任务。 培训过程完成后，当前的...

HCDP-Fast Certification Exam(HC-224)认证考试题库707q.docx

2021-04-20 上传
相比之下，MED属性（Multiple Exit Discriminators）虽然重要，但并不属于此类。 **例题解析：** - **题目:** 下列哪个BGP属性不属于众所周知的强制属性？ - **选项:** A. Origin B. Next-hop C. AS-Path D. MED ...

CMS实验：13 TeV质子-质子碰撞中单顶夸克与反夸克生成横截面测量

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为了从背景噪声中区分出顶夸克和反夸克的信号，他们应用了不同种类的喷注（jet）和b喷注（b jet）多重性，以及多元鉴别符（multivariate discriminators）。这种方法能够有效提高信号与背景的区分度，从而更准确地...

【Theoretical Deepening】: Cracking the Convergence Dilemma of GANs: In-Depth Analysis from Theory ...

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# Deep Dive into the Convergence Challenges of GANs: ...Generative Adversarial Networks (GANs) represent a significant breakthrough in the field of deep learning in recent years. They consist o

Transfer Learning and Multilayer Perceptrons (MLP): Empowering with Pre-trained Models for Rapid ...

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# Introduction to Transfer Learning and Multilayer Perceptron (MLP): Empowering High-Performance Models with Pre-trained Models, Saving Time and Resources ## 1. Introduction to Transfer Learning and ...

[Practical Guide]: Building a GAN Model from Scratch: Step-by-Step Optimization for Your First AI ...

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Generative Adversarial Networks (GANs) represent a groundbreaking technology in the field of deep learning. They consist of two neural networks—the generator and the discriminator—engaging in a game...

【Optimization Algorithms】: Tips for Enhancing GAN Stability: Creating More Robust Generative ...

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Generative Adversarial Networks (GANs), a groundbreaking technology in the field of deep learning, have achieved significant results in various areas, including image generation, text-to-image ...

【Application Extension】: The Potential of GAN in Speech Synthesis: Welcoming a New Era of Voice AI

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Since its introduction in 2014, Generative Adversarial Networks (GANs) have become a hot topic in the field of deep learning. GANs consist of a generator and a discriminator, which oppose and learn ...
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