搜索资源列表
TWSVM
- 借助(线性)孪生支持向量机对二分类问题的求解程序。-Using twin support vector machines to solve binary classification problems.
multi-class-problem
- 将多类别问题分解成多个二类别问题是解决多类别分类问题的常用方式。传统one against all（OAA）分解方式的性能更多的依赖于个体分类器的精度，而不是它的差异性。本文介绍一种基于集成学习的适于多类问题的神经网络集成模型，其基本模块由一个OAA方式的二类别分类器和一个补充多类分类器组成。测试表明，该模型在多类问题上比其他经典集成算法有着更高的精度，并且有较少存储空间和计算时间的优势。-Decompose multi-class problem into multiple binary cl
TWSVM(2)
- 借助（非线性）孪生支持向量机，来解决二分类问题。-Twin support vector machine to solve the problem of binary classification with the (non-linear).
zdjg
- 自己编写的一个，小型的最大间隔法求二分类问题的程序。-I have written a small maximum interval Method to solve two classification procedures.
BP
- 一个简单的神经网络实现二分类问题，里面包含代码，以及训练集和测试集的数据，可以直接用的，对初学者是不错的资源-A simple neural network to achieve the two-class problem, which contains the code, as well as training and testing data sets can be directly used, is a good resource for beginners
GA
- 1利用SOMF网络对二维空间中的点集进行分类2自然二进制码、格雷码与十进制码之间的相互转换3GA算法解决最小生成树问题-A two-dimensional space using the SOMF network to classify the set of points in two natural binary, Gray code with decimal code conversion between 3GA algorithm to solve the minimum spanning
wine_SVM
- 基于支持向量机的二分类模型，有效的解决了红酒分类问题-SVM binary classification model based on effective solution to the problem of wine classification
5
- BP神经网络 ，用于数据预测和曲线拟合等二分类问题-BP neural network for data prediction and curve fitting and other two classification problems
3
- BP神经网络相关算法用于二分类处理问题，不棒性比较好-BP neural network for speech recognition and pattern recognition, image recognition, etc.
nn_classification
- 使用单隐层神经网络进行二分类 使用python语言，先生成一个数据集，无法（但尝试）用logistic回归对数据集进行二分类，最后使用单隐层神经网络对数据集进行分类(classify a dataset with a 3-dimensional hidden layer)
htru2
- 对脉冲星的二分类问题，利用逻辑回归，里面有详细的注解(For the two classification problem of pulsars, there are detailed annotations.)
adaboost
- 程序为一种分类算法AdaBoost算法，用于解决二分类问题(The program is a classification algorithm AdaBoost algorithm to solve the two classification problem)