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GENERATING NEW MACHINE LEARNING MODELS BASED ON COMBINATIONS OF HISTORICAL FEATURE-EXTRACTION RULES AND HISTORICAL MACHINE-LEARNING MODELS
GENERATING NEW MACHINE LEARNING MODELS BASED ON COMBINATIONS OF HISTORICAL FEATURE-EXTRACTION RULES AND HISTORICAL MACHINE-LEARNING MODELS
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机译:基于历史特征提取规则和历史机器学习模型相结合的新机器学习模型的生成
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摘要
Techniques for generating new machine learning (ML) systems are described. In an example, a computer system receives a request specifying a task and a performance metric for the new ML model via a user interface. In response, the computer system dynamically generates new feature-extraction rules and new machine learning models based on a rule-model combination that would perform the specified task at a level meeting or exceeding the performance metric.
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