Description: Parzen概率神经网络工具箱及代码实现分类，供学习应用者参考。-Parzen Probabilistic Neural network toolbox and code to achieve the classification for the study and application in Taiwan. Platform: |
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Author:zangtianlei |
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Description: matlab环境中 关于概率神经网络源代码 注意不是关于声音识别方面的-matlab environment, probabilistic neural network source code on the note is not about the voice recognition aspects Platform: |
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Author:imella |
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Description: 概率神经网络，程序短小精悍，我已经利用该程序发表多篇论文，需要的请下载！-The probabilistic neural network, the program short and pithy。 I have published some papers needed using the code, please download! Platform: |
Size: 1024 |
Author:XIAO |
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Description: 【matlab国外编程代做】概率神经网络源码（matlab） 可以作为参考使用学习-[Do] matlab programming abroad on behalf of the probabilistic neural network source code (matlab) can be used as a reference study Platform: |
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Author:童童 |
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Description: 1.基于概率神经网络的手写体数字识别
2.径向基网络预测地下水位
3.BP神经网络实现图像压缩
4.Elman网络预测上证股市开盘价
5.基于自组织特征映射网络的亚洲足球水平聚类-1. Identify 2. RBF neural network forecasting of groundwater 3.BP neural network image compression 4.Elman network forecasting the stock market opened on 5. certificate based on self-organizing feature map of Asian football clustering based on probabilistic neural network handwritten digital Platform: |
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Author:石竹 |
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Description: 概率神经网络的matlab源代码，可用于实现概率神经网络识别和判别数据类别。-Probabilistic neural network matlab source code, can be used to achieve probabilistic neural network identification and classification of data categories. Platform: |
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Author:zhch78 |
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Description: Artificial intelligence (AI) is concerned with building systems that simulate intelligent
behavior. It encompasses a wide range of approaches, including those based on logic,
search, and probabilistic reasoning. Machine learning is a subset of AI that learns to
make decisions by fitting mathematical models to observed data. This area has seen
explosive growth and is now (incorrectly) almost synonymous with the term AI.
A deep neural network is one type of machine learning model, and when this model is
fitted to data, this is referred to as deep learning. At the time of writing, deep networks
are the most powerful and practical machine learning models and are often encountered
in day-to-day life. It is commonplace to translate text from another language using a
natural language processing algorithm, to search the internet for images of a particular
object using a computer vision system, or to converse with a digital assistant via a speech
recognition interface. All of these applications are powered by deep learning.
As the title suggests, this book aims to help a reader new to this field understand
the principles behind deep learning. The book is neither terribly theoretical (there are
no proofs) nor extremely practical (there is almost no code). The goal is to explain the
underlying ideas; after consuming this volume, the reader will be able to apply deep
learning to novel situations where there is no existing recipe for success.
Machine learning methods can coarsely be divided into three areas: supervised, unsupervised, and reinforcement learning. At the time of writing, the cutting-edge methods
in all three areas rely on deep learning (figure 1.1). This introductory chapter describes
these three areas at a high level, and this taxonomy is also loosely reflected in the book’s
organization. Platform: |
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Author:ihaveap1 |
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Search - probabilistic neural network code