Method and apparatus for predicting based on multi-source heterogeneous data
First Claim
1. A computer-implemented method for event predicting via machine learning based on multi-source heterogeneous data, comprising:
- acquiring at least two types of historical data associated with an event result for a first event of a predetermined type;
establishing a joint likelihood model of attribute data of the first event and the at least two types of historical data;
determining an optimal estimation of the attribute data based on the joint likelihood model according to a maximum posterior principle; and
determining a parameter in a probability distribution as a prediction result of a second event based on the probability distribution associated with the attribute data in the joint likelihood model, wherein the joint likelihood model includes one or more adjustment parameters for correcting the joint likelihood model, the adjustment parameters being determined iteratively based on an accuracy of the prediction result.
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Abstract
A method and apparatus for predicting based on multi-source heterogeneous data. The method comprises: acquiring, with regard to an event of a set type, at least two types of historical data that can reflect an event result; establishing a joint likelihood model of attribute data of the event of the set type and the historical data; determining an optimal estimation of the attribute data according to a maximum posterior principle; and determining, based on a probability distribution associated with the attribute data in the joint likelihood model, a parameter in the probability distribution as a prediction result of a predicted event of the set type. Some embodiments use a hierarchical model to introduce data of different sources into different data layers, unify heterogeneous data in a joint likelihood model to perform analysis, and obtain a more accurate, instant and stable prediction result through effective fusion.
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Citations
20 Claims
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1. A computer-implemented method for event predicting via machine learning based on multi-source heterogeneous data, comprising:
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acquiring at least two types of historical data associated with an event result for a first event of a predetermined type; establishing a joint likelihood model of attribute data of the first event and the at least two types of historical data; determining an optimal estimation of the attribute data based on the joint likelihood model according to a maximum posterior principle; and determining a parameter in a probability distribution as a prediction result of a second event based on the probability distribution associated with the attribute data in the joint likelihood model, wherein the joint likelihood model includes one or more adjustment parameters for correcting the joint likelihood model, the adjustment parameters being determined iteratively based on an accuracy of the prediction result. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An apparatus for event predicting via machine learning based on multi-source heterogeneous data, comprising:
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a processor; and a memory having one or more programs stored thereon for instructing said processor, the one or more programs including; instruction for acquiring, with regard to an event of a set type, at least two types of historical data that can reflect an event result; instruction for establishing a joint likelihood model of attribute data of the event of the set type and the at least two types of historical data and determining an optimal estimation of the attribute data based on the joint likelihood model according to a maximum posterior principle; and instruction for determining, with regard to an event to be predicted which is of the set type, based on a probability distribution associated with the attribute data in the joint likelihood model, a parameter in the probability distribution as a prediction result of the event to be predicted, wherein the joint likelihood model includes one or more adjustment parameters for correcting the joint likelihood model, the adjustment parameters being determined iteratively based on an accuracy of the prediction result. - View Dependent Claims (12, 13, 14, 15, 16, 17)
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18. A non-transitory computer storage medium including at least one program for event predicting via machine learning based on multi-source heterogeneous data when implemented by a processor, comprising:
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instruction for acquiring at least two types of historical data associated with an event result for a first event of a predetermined type; instruction for establishing a joint likelihood model of attribute data of the first event and the at least two types of historical data; instruction for determining an optimal estimation of the attribute data based on the joint likelihood model according to a maximum posterior principle; and instruction for determining a parameter in a probability distribution as a prediction result of a second event based on the probability distribution associated with the attribute data in the joint likelihood model, the second event to be predicted based on the probability distribution and having a preselected type of the second event that is identical to the predetermined type of the first event, wherein the joint likelihood model includes one or more adjustment parameters for correcting the joint likelihood model, the adjustment parameters being determined iteratively based on an accuracy of the prediction result. - View Dependent Claims (19, 20)
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Specification