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[AI-NN-PR] AntIDE DL : 0: 我自己写的蚁群算法IDE，用到了管道技术，界面简单，实用。-I wrote it myself ant colony algorithm IDE, use the pipeline technology, the interface is simple and practical.
Date : 2025-12-21 Size : 3.63mb User : zhangmeng
[AI-NN-PR] java_evolutionary_algorithms DL : 0: 用Java实现的进化算法包。包括遗传算法、粒子群算法、memetic算法和进化策略算法。-evolutionary-algorithm Evolutionary Algorithm package implemented using Java. The package serves as a foundation class library, supporting the implementation many variants of Evolutionary Algorithms, currently including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Memetic Algorithm (MA), Evolution Strategy (ES). Highlighted features · Support both binary & real-coded string representations of solution · Operator-based design for flexibility · EA Operators: Selection, Crossover, Mutation, Move operators in PSO & and the adaptive scheme in EA · Individual learning: Davidon–Fletcher–Powell (DFP) and Davies, Swann, and Campey with Gram-Schmidt orthogonalization (DSCG) strategies and Random Mutation Hill-climbing (RMHC) In addition, algorithm pipeline which is specified by XML file is also provided for practitioner to configure & design evolutionary algorithms at ease. User can edit runtime & algorithm parameters in the configuration file (XML) & issue the co
Date : 2025-12-21 Size : 102kb User : 陈雷
[AI-NN-PR] he-pipeline-processing-stages DL : 0: n个任务在流水线上进行m个阶段的加工，每一阶段至少有一台机器且至少有一个阶段存在多台机器，并且同一阶段上各机器的处理性能相同，在每一阶段各任务均要完成一道工序，各任务的每道工序可以在相应阶段上的任意一台机器上加工，已知任务各道工序的处理时间，要求确定所有任务的排序以及每一阶段上机器的分配情况，使得调度指标(一般求Makespan)最小。-n-m on the task in the pipeline processing stages, each stage has at least one machine there and at least one stage of multiple machines and each machine on the same stage of the processing performance of the same, at each stage required to complete each task a process, the task of each process in the corresponding stage of any one of the machine processing, known task processing time for each process, identify all tasks required for each stage of the sorting and distribution of the machine, making the scheduling Index (General requirements Makespan) minimum.
Date : 2025-12-21 Size : 3kb User : guoxiangwang
[AI-NN-PR] Undersea-pipeline-design DL : 0: 海底管道设计某石油公司在墨西哥海湾拥有几个石油钻井平台，每个平台开采出的石油需要运往路易斯安那的炼油厂。要在平台与路易斯安那海岸之间建造一个管道网，使石油通过管道传输。这管道网该如何设计，才能使建造费用最低。-Undersea pipeline design
Date : 2025-12-21 Size : 310kb User : 海春
[AI-NN-PR] Submarine-pipeline-design DL : 0: 要在平台与路易斯安那海岸之间建造一个管道网，使石油通过管道传输。这管道网该如何设计，才能使建造费用最低。-Submarine pipeline design
Date : 2025-12-21 Size : 312kb User : 李楠
[AI-NN-PR] ann_predict DL : 0: 使用人工神经网络，将样本输入网络后，进行预测。绘制预报曲线，地上管网急修作业类型-The use of artificial neural networks, the sample input network, to predict. Prediction curve drawn, the type of ground pipeline emergency repair work
Date : 2025-12-21 Size : 2kb User : 祝建英
[AI-NN-PR] zuiduanlujing DL : 0: 运输问题能够实现优化从而帮助我们找到最短路径--Pipeline transportation problem can be optimized in order to help us find the shortest path
Date : 2025-12-21 Size : 4kb User : 贾铭
[AI-NN-PR] squeezeDet-master DL : 1: 一种应用于目标检测中同时满足上述所有约束条件的全卷积神经网络结构。在我们的网络中，使用卷积层不只是用来提取特征图，同时也是作为输出层去计算边界框（bounding box）和分类概率。我们模型中的检测管道（detection pipeline）只包含一个神经网络的前向通路，因此它运行起来是极其迅速的。我们的模型是全卷积结构的，因此可以达到小的模型规模和很高的能量利用效率。最后的实验表明我们的模型能达到很高的精度，在 KITTI 基准上达到了最高的精确度。(A fully convoluted neural network structure applied to target detection simultaneously satisfying all the above constraints. In our network, using convolution layer is not only used to extract characteristic figure, but also as the output layer to compute bounding box (bounding box) and the classification probability. The detection pipeline in our model contains only a forward path of a neural network, so it runs extremely quickly. Our model is fully convoluted, so small model sizes and high energy efficiency can be achieved. The experimental results show that our model can achieve high precision, and achieve the highest accuracy on the KITTI benchmark.)
Date : 2025-12-21 Size : 2.38mb User : 123xy
[AI-NN-PR] sklearn-SVM DL : 1: 支持向量机(SVM)——分类预测，包括核函数调参，不平衡数据问题，特征降维，网格搜索，管道机制，学习曲线，混淆矩阵，AUC曲线等(Support vector machine (SVM) - classification prediction, including kernel function parameter adjustment, unbalanced data problem, feature dimensionality reduction, grid search, pipeline mechanism, learning curve, confusion matrix, AUC curve, etc)
Date : 2025-12-21 Size : 24kb User : xning
[AI-NN-PR] Python机器学习基础教程(完整电子版) DL : 0: 本书是机器学习入门书，以Python语言介绍。主要内容包括：机器学习的基本概念及其应用；实践中最常用的机器学习算法以及这些算法的优缺点；在机器学习中待处理数据的呈现方式的重要性，以及应重点关注数据的哪些方面；模型评估和调参的高级方法，重点讲解交叉验证和网格搜索；管道的概念；如何将前面各章的方法应用到文本数据上，还介绍了一些文本特有的处理方法。(This book is an introduction to machine learning, introduced in Python language. The main contents include: the basic concepts and applications of machine learning; the most commonly used machine learning algorithms in practice and their advantages and disadvantages; the importance of presenting the data to be processed in machine learning, and what aspects of data should be focused on; advanced methods of model evaluation and parameter adjustment, focusing on cross validation and grid search; the concept of pipeline; how to The methods in the previous chapters are applied to text data, and some text specific processing methods are also introduced.)
Date : 2025-12-21 Size : 13.38mb User : bridgeplq