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Description: 简单的支持向量机回归应用，应用于球磨机实验中。(Simple support vector machine regression application)
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Size: 1024 |
Author: 小克· |
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Description: 该文件为粒子群算法优化支持向量机模型(This document is optimized by Particle Swarm Optimization (SVM) model)
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Size: 28672 |
Author: zhouyucai |
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Description: LIBSVM是台湾大学林智仁(Lin Chih-Jen)教授等开发设计的一个简单、易于使用和快速有效的SVM模式识别与回归的软件包，他不但提供了编译好的可在Windows系列系统的执行文件，还提供了源代码，方便改进、修改以及在其它操作系统上应用；该软件对SVM所涉及的参数调节相对比较少，提供了很多的默认参数，利用这些默认参数可以解决很多问题；并提供了交互检验(Cross Validation)的功能。该软件可以解决C-SVM、ν-SVM、ε-SVR和ν-SVR等问题，包括基于一对一算法的多类模式识别问(LIBSVM is a simple, easy to use and fast and efficient software package for SVM pattern recognition and regression developed by Professor Lin Zhiren (Lin Chih-Jen) of National Taiwan University. He not only provides the compiled executable files in the Windows system, but also provides the source code for the convenience of improvement, modification and Application on other operating systems; the software regulates the parameters involved in SVM. Relatively few, provide a lot of default parameters, using these default parameters to solve a lot of problems; and provide the function of Cross Validation. This software can solve the problem of C-SVM, V -SVM, e -SVR and V -SVR, including a multi class pattern recognition algorithm based on Q)
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Size: 253952 |
Author: impressive |
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Description: 在线svr基于matlab的一个实现。很有用哦(A usful realizition of online-svr for matlab)
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Size: 33792 |
Author: whroc |
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Description: 基于时间序列分析ARIMA和SVR组合模型的预测(Prediction of ARIMA and SVR combined models based on time series analysis)
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Size: 134144 |
Author: yongqiang123 |
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Description: 基于遗传算法寻优参数的支持向量机算法，使得算法的参数达到全局最优，避免陷入具备最小(Support Vector Machine Algorithms Based on Genetic Algorithms for Optimizing Parameters)
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Size: 1024 |
Author: Kirrym |
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Description: SVR optimizaton using PSO with LibSVM library
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Size: 106496 |
Author: Malek01 |
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Description: 灰狼算法优化的支持向量回归，可以用于预测。(The grey Wolf algorithm is optimized for support vector regression and can be used for prediction.)
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Size: 131072 |
Author: 赵哈哈1 |
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Description: This project focuses on the energy consumption of electrical appliances which are normally used in a low energy consumption house. An IOT based Wireless Sensor is used to track the temperature and humidity conditions of the rooms of the house which are used to predict the electrical consumption of the house appliances. It is also significantly impacted by the weather conditions. In this paper, we try to predict the energy consumption of the appliances using various regression analysis models like Linear Regression, Random Forest, Support Vector Regressor(SVR), K-Neighbors Regressor and so on.
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Size: 2601471 |
Author: praba82 |
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