We address the problem of predicting a word from previous words in a sample of text. In particular, we discuss n-gram models based on classes of words. We also discuss several statistical algorithms for assigning words to classes based on the frequency of their co-occurrence with other words. We find that we are able to extract classes that have the flavor of either syntactically based groupings or semantically based groupings, depending on the nature of the underlying statistics.
标签： predicting particular previous address
上传时间： 2016-12-26
上传用户：xfbs821
The Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG are finalising a new standard for the coding (compression) of natural video images. The new standard [1,2] will be known as H.264 and also MPEG-4 Part 10, “Advanced Video Coding”. This document describes the methods of predicting intra-coded macroblocks in an H.264 CODEC.
标签： finalising standard Joint ITU-T
上传时间： 2013-12-28
上传用户：guanliya
base implementaion of the protein (starting from the amino acid sequence) feature extractor used in "L. Nanni and A. Lumini, An ensemble of Support Vector Machines for predicting virulent proteins, Expert Systems With Applications, vol.36, no.4, pp.7458-7462, May 2009. "
标签： implementaion extractor the starting
上传时间： 2017-08-02
上传用户：wqxstar
Modeling and simulation of nonlinear systems provide communication system designers with a tool to predict and verify overall system performance under nonlinearity and complex communication signals. Traditionally, RF system designers use deterministic signals (discrete tones), which can be implemented in circuit simulators, to predict the performance of their nonlinear circuits/systems. However, RF system designers are usually faced with the problem of predicting system performance when the input to the system is real-world communication signals which have a random nature.
标签： Nonlinear_Distortion_in_Wireless_ Systems
上传时间： 2020-05-31
上传用户：shancjb
这是我在做大学教授期间推荐给我学生的一本书，非常好，适合入门学习。《python深度学习》由Keras之父、现任Google人工智能研究员的弗朗索瓦•肖莱（François Chollet）执笔，详尽介绍了用Python和Keras进行深度学习的探索实践，包括计算机视觉、自然语言处理、产生式模型等应用。书中包含30多个代码示例，步骤讲解详细透彻。作者在github公布了代码，代码几乎囊括了本书所有知识点。在学习完本书后，读者将具备搭建自己的深度学习环境、建立图像识别模型、生成图像和文字等能力。但是有一个小小的遗憾：代码的解释和注释是全英文的，即使英文水平较好的朋友看起来也很吃力。本人认为，这本书和代码是初学者入门深度学习及Keras最好的工具。作者在github公布了代码，本人参照书本，对全部代码做了中文解释和注释，并下载了代码所需要的一些数据集（尤其是“猫狗大战”数据集），并对其中一些图像进行了本地化，代码全部测试通过。（请按照文件顺序运行，代码前后有部分关联）。以下代码包含了全书约80%左右的知识点，代码目录：2.1: A first look at a neural network（ 初识神经网络）3.5: Classifying movie reviews（电影评论分类：二分类问题）3.6: Classifying newswires（新闻分类：多分类问题 ）3.7: predicting house prices（预测房价：回归问题）4.4: Underfitting and overfitting（ 过拟合与欠拟合）5.1: Introduction to convnets（卷积神经网络简介）5.2: Using convnets with small datasets（在小型数据集上从头开始训练一个卷积网络）5.3: Using a pre-trained convnet（使用预训练的卷积神经网络）5.4: Visualizing what convnets learn（卷积神经网络的可视化）
上传时间： 2022-01-30
上传用户：
