This disclosure relates to learning from distributed data. In particular, it relates to determining multiple first training samples from multiple first data samples. Each of the multiple first data samples comprises multiple first feature values and a first label that classifies that first data sample. A processor determines each of the multiple first training samples by selecting a first subset of the multiple first data samples such that the first subset comprises data samples with corresponding one or more of the multiple first feature values, and combining the first feature values of the data samples of the first subset based on the first label of each of the first data samples of the first subset. The resulting training samples can be combined with training samples from other databases that share the same corresponding features and entity matching is unnecessary.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for determining multiple first training samples from multiple first data samples, each of the multiple first data samples comprising multiple first feature values and a first label that classifies that first data sample, the method comprising: determining each of the multiple first training samples by selecting a first subset of the multiple first data samples such that the first subset comprises data samples with corresponding one or more of the multiple first feature values, and combining the first feature values of the data samples of the first subset based on the first label of each of the first data samples of the first subset. 2. The method of claim 1, wherein the multiple first data samples are data samples of a first data set, and the corresponding one or more of the multiple first feature values are also corresponding feature values of a second subset of multiple second data samples of a second data set. 3. The method of claim 1, further comprising: combining two or more of the multiple first training samples to thereby determine combined training samples. 4. The method of claim 3, wherein combining the two or more of the multiple first training samples comprises adding the two or more of the multiple first training samples. 5. The method of claim 1, wherein selecting the first subset comprises selecting all of the multiple first data samples with corresponding one or more of the multiple feature values. 6. The method of claim 1, wherein selecting a first subset of the multiple first data samples comprises selecting a first subset of the multiple first data samples such that the first subset comprises data samples with corresponding two or more of the multiple first feature values. 7. The method of claim 1, wherein selecting a first subset of the multiple first data samples comprises selecting a first subset of the multiple first data samples such that the first label is identical for the first subset of the multiple first data samples. 8. The method of claim 1, wherein combining the first feature values of the data samples comprises determining a sum of the first feature values of the first data samples such that the feature value of a feature of the first training sample is the sum of the first feature values of that feature of the data samples. 9. The method of claim 8, wherein determining the sum comprises determining a weighted sum that is weighted based on the first label of each of the first data samples. 10. The method of claim 1, wherein the first data samples have signed real values as first features values, and the first label is one of ‘−1’ and ‘+1’. 11. The method of claim 1, wherein selecting the first subset comprises selecting the first subset such that the first subset comprises only data samples with identical one or more of the multiple first feature values. 12. A method for fusing a first training data set with a second training data set, the method comprising: performing the method of claim 1 for the first training data set to determine multiple first training samples; performing the method of claim 1 for the second training data set to determine multiple second training samples; and merging each of the multiple first training samples with one of the multiple second training samples that is based on the same identical feature values as that first training sample. 13. The method of claim 12, wherein merging each of the multiple first training samples with one of the multiple second training samples comprises determining a third training sample comprising the one or more identical feature values, the first feature values of that first training sample other than the corresponding one or more feature values, and the second feature values of the one of the multiple second training samples other than the corresponding one or more feature values. 14. A non-transitory computer readable medium has an executable program stored thereon that when executed causes a computer to perform the method of claim 1. 15. A computer system for determining multiple first training samples, the computer system comprising: an input port to receive multiple data samples, each of the multiple data samples comprising multiple feature values and a label that classifies that data sample; and a processor to determine each of the multiple first training samples by selecting a subset of the multiple data samples such that the subset comprises data samples with corresponding one or more of the multiple first feature values, and combining the feature values of the data samples of the subset based on the label of each of the data samples of the subset. 16. A method for determining multiple combined training samples, the method comprising: receiving multiple first training samples, each of the multiple first training samples being a combination of a subset of multiple data samples such the subset comprises data samples with corresponding one or more of multiple feature values; determining each of the multiple combined training samples by randomly selecting a subset of the multiple first training samples, and combining the feature values of the first training samples of the subset. 17. A non-transitory computer readable medium has an executable program stored thereon that when executed causes a computer to perform the method of claim 16. 18. A computer system for determining multiple combined training samples, the computer system comprising: an input port to receive multiple first training samples, each of the multiple first training samples being a combination of a subset of multiple data samples such the subset comprises data samples with corresponding one or more of multiple feature values; and a processor to determine each of the multiple combined training samples by randomly selecting a subset of the multiple first training samples, and combining the feature values of the first training samples of the subset.