Weight generation in machine learning

Abstract

Technologies are generally described for systems, devices and methods relating to a machine learning environment. In some examples, a processor may identify a training distribution of a training data. The processor may identify information about a test distribution of a test data. The processor may identify a coordinate of the training data and the test data. The processor may determine, for the coordinate, differences between the test distribution and the training distribution. The processor may determine weights based on the differences. The weights may be adapted to cause the training distribution to conform to the test distribution when the weights are applied to the training distribution.

What is claimed is: 
     
         1 . A method to improve predictive capability of a machine learning system, the method comprising:
 obtaining, by a processor of a computer, training data stored in a memory;   identifying, by the processor, a training distribution of the training data;   obtaining, by the processor, test data stored in the memory;   identifying, by the processor, information about a test distribution of the test data;   identifying, by the processor, coordinates of the training data and the test data;   determining, by the processor, for each identified coordinate, differences between the test data and the training data;   determining, by the processor, weights based on the determined differences, wherein the weights are adapted to cause the training distribution to match to the test distribution in response to the weights being applied to the training distribution;   generating, by the processor, a function based on the training distribution and the weights; and   generating labels, in response to an application of the test data to the function,   wherein generating labels in response to the application of the test data to the function facilitates matching of the test distribution and the training distribution to improve a predictive capability of the machine learning system.   
     
     
         2 . The method of  claim 1 , wherein the training distribution of the training data includes a probability distribution. 
     
     
         3 . The method of  claim 1 , wherein:
 the training data is generated at a first instance in time; and   the function is generated at a second instance in time, wherein the second instance in time is later than the first instance in time.   
     
     
         4 . The method of  claim 1 , wherein the information about the test distribution of data comprises one of a mean, standard deviation, and projection estimation of the test distribution. 
     
     
         5 . The method of  claim 1 , wherein the function is effective to determine labels as one or more of: determinations, recommendations, classifications, and predictions. 
     
     
         6 . The method of  claim 1 , wherein:
 determining the weights comprises iteratively determining differences between the training data and the test data, and   the weights are determined based on a convergent value of the determined differences between the training data and the test data.   
     
     
         7 . The method of  claim 1 , wherein:
 the training data includes a number of points,   the coordinates include a range of values in a coordinate space, and   the method further comprises:
 dividing the range of values in the coordinate space into bins; and 
 wherein determining the weights is further based on the number of points and the number of the bins. 
   
     
     
         8 . A method to improve predictive capability of a machine learning system, the method comprising, by a computer:
 identifying a training distribution of first points of training data;   identifying information about a test distribution of second points of test data;   determining a first frequency for the training distribution of the first points;   determining a second frequency for the test distribution of the second points;   comparing the first frequency and the second frequency;   calculating a weight based on differences between the first frequency and the second frequency;   generating a weighted function based on the training distribution and the calculated weight;   generating labels, in response to an application of the test data to the weighted function;   wherein the weighted function facilitates a match of a particular point in the training distribution to a particular point in the test distribution, and wherein the match improves the predictive capability of the machine learning system.   
     
     
         9 . The method of  claim 8 , wherein the test distribution includes a probability distribution. 
     
     
         10 . The method of  claim 8 , further comprising:
 identifying one or more coordinates of each of the first points and the second points;   dividing the identified one or more coordinates into one or more bins.   
     
     
         11 . The method of  claim 10 , wherein:
 the first frequency relates to a first percentage of the first points that are located within a particular bin, and   the second frequency relates to a second percentage of the second points that are located within the particular bin.   
     
     
         12 . The method of  claim 8 , wherein each of the first points of training data is evaluated by examining each point of the first points of the training data on a coordinate-by-coordinate basis. 
     
     
         13 . The method of  claim 10 , wherein comparing the first frequency and the second frequency includes:
 identifying a first comparison value;   comparing frequency values of the test data and the training data in the one or more bins to produce a difference value;   updating the first comparison value to produce a second comparison value that is based on the difference value; and   iteratively repeating the identifying the first comparison value, the comparing the frequency values of the test data and the training data in the one or more bins to produce the difference value, and the updating the first comparison value to produce the second comparison value that is based on the difference value, until the second comparison value converges to a convergent value.   
     
     
         14 . The method of  claim 12 , wherein updating the first comparison value to produce the second comparison value that is based on the difference value comprises:
 adding a fraction of the difference value to the first comparison value to produce the second comparison value.   
     
     
         15 . A computing device, comprising:
 a first processor;   a second processor; and   a memory configured to be in communication with the first processor and the second processor,   wherein the memory is effective to store training data and test data,   wherein the first processor is effective to:
 retrieve training data stored in the memory; 
 identify a training distribution of the training data; 
 retrieve test data from the memory; 
 identify information about a test distribution of the test data; 
 identify one or more coordinates of the training data and the test data 
 determine a first frequency, wherein the first frequency relates to the training data; 
 determine a second frequency, wherein the second frequency relates to the test data; 
 compare the first frequency and the second frequency; and 
 determine a weight for the training data, wherein the determination of the weight is based at least, in part, on the comparison of the first frequency and the second frequency, and 
   wherein the second processor is effective to:
 generate a weighted function based on the determined weight and the training data; and 
 operate the computing device to generate labels, in response to an application of the test data to the weighted function, wherein the generation of the labels in response to the application of the test data to the weighted function facilitates a match of the test distribution and the training distribution to improve a predictive capability of the computing device. 
   
     
     
         16 . The device of  claim 15 , wherein the one or more coordinates includes a coordinate having a range of values in a coordinate space. 
     
     
         17 . The device of  claim 15 , wherein the training data comprises first points and the test data comprises second points. 
     
     
         18 . The device of  claim 17 , wherein:
 the first points follow the training distribution,   the second points follow the test distribution, and   the weight is effective to match a particular point in the training distribution to a particular point in the test distribution.   
     
     
         19 . The computing device of  claim 16 , wherein:
 the first processor is further effective to divide the one or more coordinates into bins, and   the bins define subsets of the range of values in the coordinate space.   
     
     
         20 . The computing device of  claim 19 , wherein the first processor is further effective to:
 identify a first comparison value;   compare frequency values of the test data and the training data in the bins to produce a difference value;   update the first comparison value to produce a second comparison value that is based on the difference value;   iteratively repeat the identification of the first comparison value, the comparison of frequency values of the test data and the training data in the bins to produce the difference value, and the update of the first comparison value to produce the second comparison value that is based on the difference value, until the second comparison value converges to a convergent value; and   store the converged second comparison value in the memory.   
     
     
         21 . The computing device of  claim 19 , wherein the first processor is further effective to update the first comparison value to produce the second comparison value that is based on the difference value, by addition of a fraction of the difference value to the first comparison value. 
     
     
         22 . The computing device of  claim 15 , wherein the second processor is further effective to:
 store the weighted function in the memory.   
     
     
         23 . The device of  claim 19 , wherein the first processor is further effective to determine a weight for the training data, based on a number of first and second points located in a particular bin and based on a number of the bins.