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公开(公告)号：EP2997527A2
公开(公告)日：2016-03-23
申请号：EP14730685.6
申请日：2014-05-13
IPC分类号： G06N99/00
CPC分类号： G06Q30/0202 , G06N99/005
摘要： A training system is described herein for generating a prediction model that relies on a feature space with reduced dimensionality. The training system performs this task by producing partitions, each of which corresponds to a subset of aspect values (where each aspect value, in turn, may correspond to one or more attribute values). The training system then produces instances of statistical information associated with the partitions. Each instance of statistical information therefore corresponds to feature information that applies to a plurality of aspect values, rather than a single aspect value. The training system then trains the prediction model based on the feature information. Also described herein is a prediction module that uses the prediction model to make predictions in various online contexts.
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公开(公告)号：EP3167409A1
公开(公告)日：2017-05-17
申请号：EP15742452.4
申请日：2015-07-10
IPC分类号： G06N5/04
CPC分类号： G06K9/70 , G06K9/6262 , G06K9/6807 , G06N5/04
摘要： A service that performs automatic selection and recommendation of featurization(s) for a provided dataset and machine learning application is described. The service can be a cloud service. Selection/recommendation can cover multiple featurizations that are available for most common raw data formats (e.g., images and text data). Provided a dataset and a task, the service can evaluate different possible featurizations, selecting one or more based on performance, similarity of dataset and task to known datasets with featurizations known to have high predictive accuracy on similar tasks low predictive error, training via learning algorithms to take multiple inputs, etc. The service may include a request-response aspect that provides access to the best featurization selected for the given dataset and task.
摘要翻译： 描述了为提供的数据集和机器学习应用执行自动选择和特征推荐的服务。 该服务可以是云服务。 选择/推荐可以涵盖可用于大多数常见的原始数据格式（例如，图像和文本数据）的多个特征。 提供数据集和任务，该服务可以评估不同的可能的特征，基于性能，数据集和任务的相似性选择一个或多个已知数据集，已知具有对类似任务具有高预测精度的特征，低预测误差，通过学习算法进行训练 采取多个输入等。服务可以包括请求响应方面，其提供对为给定数据集和任务选择的最佳特征的访问。
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