Labview机器学习工具包使用实例资源-CSDN文库

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Labview机器学习工具包是NI（National Instruments）为开发者提供的一种强大的数据处理和分析平台，它结合了机器学习算法，使工程师和科学家能够在可视化环境中构建智能系统。本篇将详细介绍Labview机器学习工具包中的一些关键算法及其使用实例，包括BP神经网络、支持向量机（SVM）、自组织映射（SOM）、K-均值聚类（LVQ）、线性判别分析（LDA）、主成分分析（PCA）以及各种类聚方法。 1. **BP神经网络**：反向传播（Backpropagation, BP）神经网络是一种在多层前馈神经网络中训练权重的方法。它通过迭代调整权重来最小化预测输出与实际输出之间的误差。在Labview机器学习工具包中，BP网络可以用于复杂函数拟合、非线性建模和分类任务。 2. **支持向量机（SVM）**：SVM是一种监督学习模型，用于分类和回归分析。其核心思想是找到一个最优超平面，最大化不同类别间的间隔。在Labview中，SVM可以处理线性和非线性数据，尤其适合小样本、高维问题。 3. **自组织映射（SOM）**：SOM是一种无监督学习算法，用于创建输入数据的二维映射。它能够保留原始数据的拓扑结构，常用于数据可视化和聚类分析。在Labview中，SOM可用于理解高维数据的结构和发现模式。 4. **K-均值聚类（LVQ）**：LVQ是K-均值算法的一种变体，适用于有监督的学习任务。它通过定义多个原型（或中心点），并将样本分配到最近的原型所属的类别。Labview中的LVQ可以用来对数据进行自动分类和分组。 5. **线性判别分析（LDA）**：LDA是一种统计方法，用于降维和分类。它寻找数据的最佳投影方向，以最大化类别间的差异。在Labview中，LDA可以用于特征提取和分类任务，尤其适用于多分类问题。 6. **主成分分析（PCA）**：PCA是一种无监督的线性降维技术，通过找到数据的主要成分来减少数据的维度，同时保留大部分方差。在Labview机器学习工具包中，PCA可用来消除噪声，简化数据，并帮助可视化高维数据。 7. **类聚方法**：除了LVQ之外，Labview工具包可能还包括其他类聚算法，如层次聚类、DBSCAN等。这些算法用于无监督学习，将数据点按照相似性分组，帮助我们发现数据的内在结构。在“Machine Learning Toolkit Examples”文件中，你可以找到这些算法的实际应用案例，通过这些示例，你可以学习如何在Labview中配置和训练模型，调整参数，以及评估模型性能。通过实践，你将更好地理解这些算法的工作原理，提高你在机器学习领域的应用能力。

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Machine Learning Toolkit Examples.zip （16个子文件）

Machine Learning Toolkit Examples

Example_BP Network_Curve Fitting.vi 26KB

Example_SOM.vi 24KB

Machine Learning Toolkit User Manual.pdf 388KB

Example_Manifold Learning.vi 44KB

Example_LVQ.vi 28KB

Example_LDA.vi 38KB

Example_Clustering.vi 113KB

Example_Kernel PCA.vi 24KB

Example_BP Network_Classification.vi 28KB

Example_SVM.vi 27KB

Data

Data_Concentric Circles.vi 21KB

Data_Three Balls.vi 17KB

Data_Uniform Square.vi 13KB

Data_Swiss Roll.vi 20KB

Data_Four Gaussian.vi 20KB

Data_Hemisphere.vi 17KB

LabVIEW Machine Learning Toolkit

User Manual

1. INTRODUCTION ............................................................................................................................................. 1

2. FEATURES ...................................................................................................................................................... 1

2.1 MACHINE LEARNING ALGORITHMS ....................................................................................................................... 2

2.1.1 Unsupervised learning algorithms ........................................................................................................... 2

2.1.2 Supervised learning algorithms ............................................................................................................... 3

2.1.3 Dimension reduction algorithms .............................................................................................................. 4

2.2 VARIANT DATA TYPE .......................................................................................................................................... 5

2.3 DISTANCE/KERNEL VI REFERENCE ........................................................................................................................ 6

2.4 VALIDATION & VISUALIZATION UTILITIES ............................................................................................................... 6

3. SYSTEM REQUIREMENTS ............................................................................................................................... 6

4. INSTALLATION NOTES .................................................................................................................................... 6

TABLE I. THE APPLICABILITY OF VALIDATION AND VISUALIZATION UTILITIES TO DIFFERENT MACHINE LEARNING ALGORITHMS. “X”

INDICATES THAT A UTILITY IS APPLICABLE TO A CERTAIN ALGORITHM. ......................................................................................... 7

1. Introduction

The idea of machine learning is to mimic the learning process of human beings, i.e., gaining knowledge

through experience. Machine learning algorithms allow machines to generalize rules from empirical data,

and, based on the learned rules, make predictions for future data. The Machine Learning Toolkit (MLT)

provides various machine learning algorithms in LabVIEW. It is a powerful tool for problems such as

visualization of high-dimensional data, pattern recognition, function regression and cluster identification.

2. Features

The Machine Learning Toolkit includes algorithms, data types, validation functions, and visualization

tools.

2.1 Machine Learning Algorithms

2.1.1 Unsupervised learning algorithms

Unsupervised learning refers to the problems of revealing hidden structure in unlabeled data.

Since the data are unlabeled, there is no error signal fed back to the learner in the algorithm.

This distinguishes unsupervised learning from supervised learning.

Clustering is one of the main and important approaches of unsupervised learning. Clustering

means the assignment of class memberships to a set of objects so that similar objects are

assigned into the same class and dissimilar ones are assigned into different classes. Each class

often represents a meaningful pattern in the respective problem. Clustering is thereby useful

for identification of different patterns in data. For example, in image processing clustering

can be used to divide a digital image into distinct regions for border detection or object

recognition.

List of functions:

 k-means

 k-medians

 k-medoids

 Fuzzy C-means

 Gaussian Mixture Model (GMM)

 Hierarchical Clustering

 Spectral Clustering

 Vector Quantization (VQ)

 Self-Organizing Map (SOM)

Conceptual diagram of usage of the Machine Learning Toolkit for unsupervised learning

 Data preparation

Data need to be formatted to fit the API of the unsupervised learning function the

user selects.

 Unsupervised learning function application

An unsupervised learning function is used to learn the structure of the input data.

 Evaluation/Visualization of results

Refer to Section 2.4 for the choice of appropriate evaluation/visualization utility.

Examples:

 Example_Clustering

 Example_SOM

Data

preparation

Unsupervised

learning function

Evaluation/

Visualization of

results

2.1.2 Supervised learning algorithms

Supervised learning refers to the generalization of the relationship (function) between the

input data and their corresponding outputs (labels). The relationship (function) is learned

through a training set of examples, each of which is a pair of an input data and a desired

output. During the training, the error between the actual and the desired outputs is frequently

fed back into the system for tuning the system parameters according to certain learning rule.

The system “learns” by adapting itself to minimize the error. After the training, the

performance of the learned relationship (function) should be evaluated on a test set (of

examples) that is separate from the training set.

Supervised learning is useful for pattern recognition, function regression, etc. One example of

applications is recognition of handwritten numbers. A supervised classifier can be trained

with a reservoir of handwritten numbers, each with a label (the true number each image

represents). Having been validated on a separate test set, the trained classifier can be used for

fast and accurate recognition of future handwritten numbers.

List of functions:

 k-Nearest Neighbors (k-NN)

 Back-propagation (BP) Neural Network

 Learning Vector Quantization (LVQ)

 Support Vector Machine (SVM)

Conceptual diagram of usage of the MLT for supervised learning

 Data preparation

Data need to be formatted to fit the API of the unsupervised learning function the

user selects.

 Splitting data into training and test sets

Data

preparation

Supervised

learning

function

(learn w/

training set)

Evaluation/

Visualization

of results

Splitting data

into training

and test sets

Supervised

learning

function

(evaluate

w/ test set)

Training set

Test set

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浪人昶灵

2015-04-08

版本太高，没有打的开
zhengenhao2010

2015-07-30

这是例程，里面的module还是没有，被骗的感觉
wanglang1996

2017-01-20

Labview机器学习工具包使用实例 - 下载频道 - CSDN.NET http://download.csdn.net/detail/annywangan/6799723#comment 确实的模块在这里
l_1h

2015-11-19

正在学习，拿来用用，还可以，供学习参考！
RZhang526

2016-10-24

东西是好东西，就是里面核心模块都没有，让我泪奔了