Labview机器学习工具包使用实例

© National Instruments Corporation 1 Machine Learning Toolkit

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

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.

© National Instruments Corporation 2 Machine Learning Toolkit

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.

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

user selects.

© National Instruments Corporation 3 Machine Learning Toolkit

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.

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

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

user selects.