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Specifically, in the modern era of massive datacollections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient. Labeling, indexing, ease of discovery, and ease of access are essential if end-users are to find and benefit from the collection.
MachineLearning algorithms often need to handle highly-imbalanced datasets. This in turns makes the performance evaluation of the classifier difficult, and can also harm the learning of an algorithm that strives to maximise accuracy. A weighted nearest neighbor algorithm for learning with symbolic features. Quinlan, J.
This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as KnowledgeDiscovery from Data (KDD). Machinelearning provides the technical basis for data mining.
The interest in interpretation of machinelearning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machinelearning algorithms, and more specifically deep learning, has been gaining in various domains. Conference on KnowledgeDiscovery and Data Mining, pp.
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