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《Machine Learning Toolkit User Manual》.pdf

发布:2015-10-19约1.66万字共7页下载文档
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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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