搜索资源列表
stoneage8.0
- 石器时代8.0服务端完整源代码。可以直接编译，源码为SMO服务端。-Stone Age 8.0 server source code integrity. Can be directly compiled, source code for the SMO server.
SMO-8.0.5.1.7z
- SMO-8.1.8.24.7z 最新版SMO-8.1.8.24.7zSMO-8.1.8.24.7z-SMO-8.1.8.24.7z
shiqi8.0
- 石器时代的8.0源代码！完全可以直接开服-Stone Age 8.0 source code! Is entirely possible to open a direct service
SMO-8.1.8.24
- 石器服务端smo源码内容包括：SMO 8.0.5.1 & SMO gmsv.8.1.8.24 saac.1.07.8.24-Stone Source server smo include: SMO 8.0.5.1 & SMO gmsv.8.1.8.24 saac.1.07.8.24
SMO-8.1.8.24
- 这个是网络游戏石器时代的源码，有需要的可以下载。-This is the online game Stone Age of the source, there is a need can be downloaded.
SMO-v8.1.8.24
- SMO v8.1.8.24吉仔源码 完全可在centos下编译成功 输出win端还是linux可Makefile规则文件控制-SMO v8.1.8.24 Kat Tsai source completely successful in centos linux output side or compile win Makefile rules files can be controlled
SVM
- SVM: 一种分类器，采用最大化分类间隔进行优化参数。 关于这个分类器两点比较重要： 1）SMO优化算法需要掌握， 可以具体参看两篇文章，John Platt的文章 以及“Improvements to Platt s SMO algorithm for SVM Classifier Design” 2）核函数的使用，如何将核函数使用到SVM中，核函数就是空间转换的函数， 说白了就是距离计算函数，如何将同类之
svm_python
- 在机器学习领域，支持向量机SVM(Support Vector Machine)是一个有监督的学习模型，通常用来进行模式识别、分类、以及回归分析。本程序是SVM的python实现，用的是SMO算法。只能进行分类，并且能够显示图形结果。-In the field of machine learning, support vector machines SVM (Support Vector Machine) is a supervised
simple_smo
- 这是简单的svm算法，实现其中的SMO部分，能够直接运行，可嵌入-This is a simple SVM algorithm, which can achieve the SMO part, can be directly run, can be embedded
svmMLiA
- 支持向量机是最常用的一种分类器，它通过求解一个二次优化问题来最大化分类间隔，本例采用的SMO算法，可以大大优化运行-Support vector machine is the most commonly used classifier, it can be used to solve a two optimization problem to maximize the classification interval, this exam
Simons-Windows-8-UI-Demo
- win8 labview SMO结构的UI-win8 labview SMO UI
libsvm-3.1
- LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. Since versio
libsvm-3.22
- libsvm-3.22.rar LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
svm
- 使用smo算法实现了支持向量机的python代码(complete the svm mode and Sequential minimal optimization with python code)
SMO optimization algorithm
- SMO 优化算法 径向基核函数 代码加数据 实例 机器学习实战第六章代码(SMO optimization algorithm radial basis function)
SVM-w-SMO-master
- Simple implementation of a Support Vector Machine using the Sequential Minimal Optimization (SMO) algorithm for training.
SVM-w-SMO
- 用序列最小优化算法（SMO）进行训练的支持向量机的简单实现。(simple implementation of a Support Vector Machine using the Sequential Minimal Optimization (SMO) algorithm for training.)