Search - network growth algorithm

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[Other resource] 三角网生成 DL : 0: VC++下的不规则三角网生成算法,这是不规则三角网的生长算法,通过建立TIN类,在TIN类内实现一些方法.-VC under the TIN generation algorithm, which is triangular irregular network growth algorithm, through the establishment of TIN category, the category TIN achieve some ways.
Date : 2008-10-13 Size : 1.98kb User : feng
[Data structs] 三角网生成 DL : 0: VC++下的不规则三角网生成算法,这是不规则三角网的生长算法,通过建立TIN类,在TIN类内实现一些方法.-VC under the TIN generation algorithm, which is triangular irregular network growth algorithm, through the establishment of TIN category, the category TIN achieve some ways.
Date : 2025-07-08 Size : 2kb User : feng
[Other] BA DL : 0: 基于Matlab的BA无标度网络拓扑生成算法，BA模型有两个重要特性：增长特性和优先连接特性。-Matlab based on the BA scale-free network topology generation algorithm, BA model has two important characteristics: growth characteristics and properties of priority connections.
Date : 2025-07-08 Size : 1kb User : xiaoli
[GIS program] tin DL : 0: 不规则三角网的生长算法实现较为实用的生长算法操作简单 -Triangulated irregular network growth algorithm for a more practical realization of the growth algorithm is simple
Date : 2025-07-08 Size : 66kb User : 都海伦
[Other] PageRank_Degree_Distribution DL : 0: 基于PageRank算法的网络增长模型的度分布图像，正常运行-PageRank algorithm based on degree distribution network growth model image, normal operation
Date : 2025-07-08 Size : 4kb User : yk
[AI-NN-PR] KRLS_NC DL : 0: 经过新息准则处理过的核自适应滤波算法KLMS.该算法可以降低klms的网络增长问题。-After the new interest guidelines processed nuclear adaptive filtering algorithm KLMS. Klms this algorithm can reduce the problem of network growth.
Date : 2025-07-08 Size : 1kb User : 王仁
[Other] youfun_v54 DL : 0: 供做算法研究人员参考，三相光伏逆变并网的仿真，计算晶粒的生长，入门级别程序。- Algorithm for researchers to do reference, Three-phase photovoltaic inverter and network simulation, Calculation of growth, entry-level program grain.
Date : 2025-07-08 Size : 5kb User : wadgqped
[matlab] Matlab DL : 0: 基于Matlab的BA无标度网络拓扑生成算法，BA模型有两个重要特性：增长特性和优先连接特性(BA based scale-free network topology generation algorithm based on Matlab. There are two important characteristics of BA model: growth characteristics and priority connection characteristics.)
Date : 2025-07-08 Size : 1kb User : 小黑小白
[Other] Understanding deep learning DL : 0: 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.
Date : 2023-07-03 Size : 11.11mb User : ihaveap1