《Machine Learning Toolkit User Manual》.pdf
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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 algorithms4
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 ALGORITHM7
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.
© National Instruments Corporation 1 Machine Learning Toolkit
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 importan
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