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Using Proxy Labels, Building a Labeling System, and Utilizing a Labeling Service when Historical Labeled Data is Unavailable for ML Projects - Python Automation and Machine Learning for ICs - - An Online Book: Python Automation and Machine Learning for ICs by Yougui Liao - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= Using proxy labels, building a labeling system, and utilizing a labeling service are valid approaches when historical labeled data is unavailable for machine learning projects:
The choice among these strategies depends on several factors, including the availability of resources, the required scale of labeled data, the criticality of label accuracy, and the specific requirements of the machine learning problem at hand. Combining these strategies might be necessary to achieve the best results, such as starting with proxy labels to quickly develop initial models while concurrently building a robust labeling system for more accurate and reliable data annotation. One example is that in enhancing defect detection in silicon wafer images within semiconductor manufacturing through machine learning, a crucial step involves obtaining a high-quality labeled dataset. For specialized applications such as this, where precision in defect identification is critical, the most effective approach is to collaborate with a third-party labeling company. These companies specialize in providing accurately classified and annotated data, which is essential for training robust machine learning models. Leveraging such expert services ensures that the dataset is of the highest standard, thereby facilitating the development of highly effective defect detection systems in the semiconductor industry. ===========================================
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