METHOD OF AND SYSTEM FOR ONLINE MACHINE LEARNING WITH DYNAMIC MODEL EVALUATION AND SELECTION

    公开(公告)号:US20220027764A1

    公开(公告)日:2022-01-27

    申请号:US17385285

    申请日:2021-07-26

    Abstract: There is provided a method and system for providing a recommendation for a given problem by using a set of supervised machine learning (ML) models online by performing dynamic model evaluation and selection. An optional knowledge capture phase may be used to train the set of ML models offline using passive and/or active learning. Upon detection of a suitable initialization condition, the set of ML models is provided for inference and a feature vector is obtained. A set of predictions associated with accuracy metrics is generated by the set of models based on the feature vector. The accuracy metric may be global or class-specific. A recommendation is provided based on at least one of the set of predictions. The recommendation may be provided by selecting a best model, or by performing a vote weighted by the accuracy metrics. The set of ML models is retrained after obtaining an actual prediction.

    METHOD FOR PROVIDING TEAM-LEVEL METRICS DATA AND TEAM STATE MONITORING SYSTEM
    2.
    发明申请
    METHOD FOR PROVIDING TEAM-LEVEL METRICS DATA AND TEAM STATE MONITORING SYSTEM 审中-公开
    提供团队级数据和团队状态监测系统的方法

    公开(公告)号:US20170004435A1

    公开(公告)日:2017-01-05

    申请号:US15198783

    申请日:2016-06-30

    Abstract: A method and system are disclosed for providing team-level metrics data, the method comprising for each member of a team, collecting sensor data originating from a plurality of sensors, and locally processing the collected sensor data to provide data representative of an individual functional assessment; wirelessly obtaining each of the data representative of an individual functional assessment and processing each of the obtained data representative of an individual functional assessment to generate data representative of a functional state of the team.

    Abstract translation: 公开了一种用于提供团队级度量数据的方法和系统,所述方法包括为团队的每个成员收集源自多个传感器的传感器数据,并且本地处理所收集的传感器数据以提供表示个体功能评估的数据 ; 无线地获得代表单个功能评估的每个数据,并且处理代表各个功能评估的所获得的每个数据,以产生表示团队的功能状态的数据。

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