Method and system for configuring preprocessing for supervised learning model

A method comprising:

• acquiring a first plurality of combinations COMB_a1 to COMB_l1 that each include a variety of values on one of a plurality of parameters a to l of a first function g1 that is disposed previous to a first supervised learning model f1 that implements feedback to the first function g1, by providing training data TDi to the first supervised learning model f1, i being an integer;
• acquiring a second plurality of combinations COMB_a2 to COMB_l2 that each include a variety of values on one of a plurality of parameters a to l of a second function g2 that is disposed previous to a second supervised learning model f2 that is implements feedback to the second function g2, by providing training data TDi to the second supervised learning model f2, operation of the first supervised learning model f1 and operation of the second supervised learning model f2 being equivalent to each other;
• calculating a first plurality of distributions D_a1 to D_l1 on the first plurality of combinations COMB_a1 to COMB_l1 respectively;
• calculating a second plurality of distributions D_a2 to D_l2 on the second plurality of combinations COMB_a2 to COMB_l2 respectively;
• and
• evaluating which of the first function g1 and the second function g2 is more robust by comparing the first plurality of distributions D_a1 to D_l1 and the second plurality of distributions D_a2 to D_l2 respectively.

A method comprising:

• acquiring a plurality of combinations COMB(1) to COMB(n) that each include a plurality of values on a plurality of parameters a to l of a function g that is disposed previous to a supervised learning model f that implements feedback to the function g, by providing training data TDi to the supervised learning model f, n being an integer equal to or more than two;
• disposing the plurality of combinations COMB(1) to COMB(n) in space that has a plurality of dimensions of which number is equivalent to a number of the plurality of parameters a to l of the function g;
• acquiring at least one sampled point SP in the space;
• selecting at least one among the plurality of combinations COMB(1) to COMB(n) that is closest to the at least one sampled point SP;
• and
• assigning the plurality of values included in the selected one of the plurality of combinations COMB(1) to COMB(n) to the plurality of parameters a to l of the function g.

A device comprising:

• a first definer that defines a function g with a plurality of parameters a to l;
• a first disposer that disposes the function g previous to a supervised learning model f;
• the supervised learning model f being trained with training data TDi including an example xi and a label yi, and i being an integer;
• a second definer that defines a function f(g(xi)) by using the supervised learning model f and the function g;
• a first calculator that calculates a result Zi by calculating the function f(g(xi));
• a second calculator that calculates a difference Li between the result Zi and the label yi;
• a third calculator that calculates a partial differential PDi of the difference Li on the plurality of parameters a to l of the function g;
• an updater that updates the plurality of parameters a to l of the function g according to the partial differential PDi;
• a third definer that defines a pseudo preprocessing h by using the plurality of parameters a to l of the function g when the difference Li converges during repetition between the calculating of the result Zi and the updating of the plurality of parameters a to l of the function g;
• a second disposer that disposes the pseudo preprocessing h previous to the supervised learning model f;
• and
• a trainer that trains the supervised learning model f by providing the training data TDi to the pseudo preprocessing h.

A device comprising:

• a first acquirer that acquires a first plurality of combinations COMB_a1 to COMB_l1, each of which includes a variety of values on one of a plurality of parameters a to l of a first function g1 that is disposed previous to a first supervised learning model f1 that implements feedback to the first function g1, by providing training data TDi to the first supervised learning model f1, i being an integer;
• a second acquirer that acquires a second plurality of combinations COMB_a2 to COMB_l2, each of which includes a variety of values on one of a plurality of parameters a to l of a second function g2 that is disposed previous to a second supervised learning model f2 that is implements feedback to the second function g2, by providing training data TDi to the second supervised learning model f2, operation of the first supervised learning model f1 and operation of the second supervised learning model f2 being equivalent to each other;
• a first calculator that calculates a first plurality of distributions D_a1 to D_l1 on the first plurality of combinations COMB_a1 to COMB_l1 respectively;
• a second calculator that calculates a second plurality of distributions D_a2 to D_l2 on the second plurality of combinations COMB_a2 to COMB_l2 respectively;
• and
• an evaluator that evaluates which of the first function g1 and the second function g2 is more robust by comparing the first plurality of distributions D_a1 to D_l1 and the second plurality of distributions D_a2 to D_l2 respectively.

A device comprising:

• a first acquirer that acquires a plurality of combinations COMB(1) to COMB(n) that each include a plurality of values on a plurality of parameters a to l of a function g that is disposed previous to a supervised learning model f that implements feedback to the function g, by providing training data TDi to the supervised learning model f, n being an integer equal to or more than two;
• a disposer that disposes the plurality of combinations COMB(1) to COMB(n) in space that has a plurality of dimensions of which number is equivalent to a number of the plurality of parameters a to l of the function g;
• a second acquirer that acquires at least one sampled point SP in the space;
• a selector that selects at least one among the plurality of combinations COMB(1) to COMB(n) that is closest to the at least one sampled point SP;
• and
• an assigner that assigns the plurality of values included in the selected one of the plurality of combinations COMB(1) to COMB(n) to the plurality of parameters a to l of the function g.