搜索资源列表
ELM_kernel
- 基于极限学习机的不平衡数据集分类，性能极好，且速度较快-Based on extreme learning machine for imbalanced data sets classification, excellent performance, and faster
imbanlace_kernel
- 基于径向基核函数的不均衡数据集的极限学习机分类源代码-Based on radial basis function unbalanced datasets Extreme Learning Machine classifier source code
chapter30
- 鸢尾花的极限学习机分类，有程序，有数据,还有一个例子-Iris ultimate learning machine classification, procedures, data, and an example
image-segment
- 这是用ELM（极限学习机）做的关于图像识别的分类实验，有数据，有程序，有训练时间，测试时间和精度-It is used ELM (Extreme Learning Machine) to do experiments on the classification of image recognition, has data, procedures, training time, test time and accuracy
ClassicalELM
- 通过极限学习机的相关算法，实现数据的预测、回归、分类，从而有利益数据的处理-Processed through the relevant algorithm ELM achieve prediction data, regression, classification, and thus interest data
ELM
- 极限学习机，类似单层神经网络学习，可以用于稀疏表示中数据训练分类-extreme learning machine
breast_figure
- breast数据集的极限学习机的分类算法，自己编写的，可以运行-Extreme Learning Machine classification algorithm breast datasets, write your own, you can run
MATLAB ELM
- 几种改进极限学习机算法，可用于数据分类、故障诊断等(Several improved limit learning algorithm, can be used for data classification, fault diagnosis, etc.)
elm
- 极限学习机，分类和回归，有程序，数据和相关案例(ultimate learning machine classification and regression, procedures, data, and examples.)
极限学习机
- 极限学习机分类器，训练函数与预测函数，以及数据实例(Extreme Machine Classifiers, Training and Prediction Functions, and Data Instances)
ELM
- 采用python语言实现极限学习机算法，数据三分类，加入数据可以跑通(Using python language to achieve extreme learning machine algorithm, data three classification, join data can run through)
matlab1
- 通过极限学习机对数据进行二分类，亲测有效好使(Two classifications of data by extreme learning machine.)
H-ELM
- 可用作数据分类和拟合，深度极限学习机拥有深度学习的优势和自身计算速度快的优势(It can be used to classify and fit data. The deep extrme learning machine has the advantages of depth learning and fast computing speed.)
KELM
- 可用作数据的拟合和分类。核极限学习机采用了核函数，将数据投射到高维空间分类(It can be used for data fitting and classification. Kernel extreme learning machine uses kernel function to project data onto high-dimensional space.)
基于极限学习机ELM的数据分类
- 针对数据分类问题，提出了基于极限学习机的分类方法，将数据样本分为训练样本和测试样本，并采用准确率指标进行评价。(Aiming at the problem of data classification, a classification method based on extreme learning machine is proposed. The data samples are divided into training samples and test samples, and the
ELM
- 用于光谱数据的分类，极限学习机是针对单隐层前馈神经网络提出的新算法。相对于传统的神经网络算法，不仅保证了学习精度，而且提高了学习速度。(Classification of spectral data The extreme learning machine is a new algorithm proposed for the single hidden layer feedforward neural network. Compared with the traditional neural
ELM分类
- 内含两个数据集---iris_data和classsim，分别为艾瑞斯花和红酒的分类训练数据。分别用这两个数据集对极限学习机（ELM）进行训练，并测试ELM的分类效果。(It contains two data sets, iris_data and classsim, which are classified training data of Iris Flower and Red Wine respectively. The two data sets are used to train t