研究目的
Reviewing and illustrating the application of supervised and unsupervised machine learning algorithms, specifically Support Vector Machine (SVM) for classification and K-means for clustering, in the context of optical networks to improve performance and understand network behavior.
研究成果
The paper concludes that SVM and K-means algorithms are beneficial for optical network control and management, with SVM useful for soft-failure detection and K-means for discovering patterns in unlabeled data for visualization purposes.
研究不足
The paper does not explicitly mention limitations but focuses on illustrative examples rather than comprehensive testing or application across diverse scenarios.
1：Experimental Design and Method Selection:
The paper reviews SVM for classification and K-means for clustering, illustrating their application in optical networks.
2：Sample Selection and Data Sources:
Uses datasets for SVM to classify filter-related soft failures and K-means for clustering lightpaths BER monitoring data.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned.
4：Experimental Procedures and Operational Workflow:
Describes the process of applying SVM and K-means algorithms to the datasets.
5：Data Analysis Methods:
Uses SVM for binary classification and K-means for clustering, with visualization techniques for data interpretation.
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