A learning apparatus includes at least one memory and at least one circuit. The circuit (a) obtains a first neural network that has learned by using source learning data and obtains target learning data, the target learning data including a plurality of first data items each of which is given a first label and a plurality of second data items each of which is given a second label, (b) obtains a plurality of first output vectors by inputting the plurality of first data items to a second neural network and obtains a plurality of second output vectors by inputting the plurality of second data items to the second neural network, and (c) generates a first relation vector corresponding to the first label by using the plurality of first output vectors and generates a second relation vector corresponding to the second label by using the plurality of second output vectors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning apparatus comprising: at least one memory; and at least one circuit, wherein the circuit (a) obtains a first neural network that has learned by using source learning data which is learning data with answer labels in a source domain and obtains target learning data which is learning data with answer labels in a target domain, the target learning data including a plurality of first data items each of which is given a first label and a plurality of second data items each of which is given a second label, (b) obtains a plurality of first output vectors by inputting the plurality of first data items to a second neural network and obtains a plurality of second output vectors by inputting the plurality of second data items to the second neural network, the first neural network and the second neural network having identical weighting values, identical numbers of hidden layers, identical numbers of neurons in each of the hidden layers, identical numbers of neurons in an input layer, and identical numbers of neurons in an output layer, and (c) generates a first relation vector corresponding to the first label by using the plurality of first output vectors and generates a second relation vector corresponding to the second label by using the plurality of second output vectors. 2. The learning apparatus according to claim 1, wherein (i) an output vector obtained by inputting a first data item that is given the first label to the second neural network is closer to the first relation vector than to the second relation vector, and (ii) an output vector obtained by inputting a second data item that is given the second label to the second neural network is closer to the second relation vector than to the first relation vector. 3. The learning apparatus according to claim 1, wherein the circuit (d) updates weight information on the second neural network by using the first relation vector and the second relation vector, and in (d), the circuit updates the weight information by performing learning in which the plurality of first output vectors that are outputs for the plurality of first data items take the first relation vector as an answer, and the plurality of second output vectors that are outputs for the plurality of second data items take the second relation vector as an answer. 4. The learning apparatus according to claim 1, wherein in (c), the circuit generates an average vector of the plurality of first output vectors as the first relation vector and generates an average vector of the plurality of second output vectors as the second relation vector. 5. The learning apparatus according to claim 3, wherein in (c), the circuit adjusts a value of the first relation vector and a value of the second relation vector so that the value of the first relation vector is within a range of a constant multiple of a first standard deviation calculated from the plurality of first output vectors, the value of the second relation vector is within a range of a constant multiple of a second standard deviation calculated from the plurality of second output vectors, and a difference value between the first relation vector and the second relation vector is increased. 6. The learning apparatus according to claim 1, wherein the learning apparatus holds a plurality of sets of source learning data which is learning data with answer labels, and distance information for calculating similarities among the answer labels, and the circuit (e) calculates distances between all answer labels given to the plurality of sets of source learning data and all answer labels given to the target learning data and selects a set of source learning data having a shortest distance from among the plurality of sets of source learning data, (f) generates the first neural network through learning using the selected set of source learning data, and (g) stores structure information and weighting values of the generated first neural network in the memory. 7. The learning apparatus according to claim 1, wherein the target learning data includes a two-dimensional electrophoretic profile of proteins separated based on an isoelectric point and a molecular weight, the first label indicates sepsis, and the second label indicates non-sepsis. 8. The learning apparatus according to claim 7, wherein the source learning data includes a two-dimensional electrophoretic profile of proteins separated based on an isoelectric point and a molecular weight in a healthy person, and an answer label of the source learning data indicates a healthy person. 9. An identifying apparatus comprising: a first circuit, wherein the first circuit (v) obtains a first relation vector and a second relation vector from a learning apparatus including a second circuit, wherein the second circuit (a) obtains a first neural network that has learned by using source learning data which is learning data with answer labels in a source domain and obtains target learning data which is learning data with answer labels in a target domain, the target learning data including a plurality of first data items each of which is given a first label and a plurality of second data items each of which is given a second label, (b) obtains a plurality of first output vectors by inputting the plurality of first data items to a second neural network and obtains a plurality of second output vectors by inputting the plurality of second data items to the second neural network, the first neural network and the second neural network having identical weighting values, identical numbers of hidden layers, identical numbers of neurons in each of the hidden layers, identical numbers of neurons in an input layer, and identical numbers of neurons in an output layer, and (c) generates the first relation vector corresponding to the first label by using the plurality of first output vectors and generates the second relation vector corresponding to the second label by using the plurality of second output vectors, and wherein the first circuit (w) obtains test data which is data without a label and of a type identical to the target learning data, (x) obtains an output vector for the obtained test data by inputting the test data to the second neural network, (y) calculates a distance between the obtained output vector and the first relation vector and a distance between the obtained output vector and the second relation vector, and (z) outputs, as an answer label for the test data, the first label or the second label corresponding to one of the first relation vector and the second relation vector that is closer to the output vector. 10. A learning and identifying system comprising: a learning apparatus including at least one memory and at least one first circuit; and an identifying apparatus including at least one second circuit, wherein the first circuit of the learning apparatus (a) obtains a first neural network that has learned by using source learning data which is learning data with answer labels in a source domain and obtains target learning data which is learning data with answer labels in a target domain, the target learning data including a plurality of first data items each of which is given a first label and a plurality of second data items each of which is given a second label, (b) obtains a plurality of first output vectors by inputting the plurality of first data items to a second neural network and obtains a plurality of second output vectors by inputting the plurality of second data items to the second neural network, the first neural network and the second neural network having identical weighting values, identical numbers of hidden layers, identical numbers of neurons in each of the hidden layers, identical numbers of neurons in an input layer, and identical numbers of neurons in an output layer, and (c) generates a first relation vector corresponding to the first label by using the plurality of first output vectors and generates a second relation vector corresponding to the second label by using the plurality of second output vectors, and wherein the second circuit of the identifying apparatus (v) obtains the first relation vector and the second relation vector from the learning apparatus, (w) obtains test data which is data without a label and of a type identical to the target learning data, (x) obtains an output vector for the obtained test data by inputting the test data to the second neural network, (y) calculates a distance between the obtained output vector and the first relation vector and a distance between the obtained output vector and the second relation vector, and (z) outputs, as an answer label for the test data, the first label or the second label corresponding to one of the first relation vector and the second relation vector that is closer to the output vector. 11. A non-transitory computer-readable recording medium storing a control program for causing a device including a processor to execute a process, the process causing a circuit included in a learning apparatus to (a) obtain a first neural network that has learned by using source learning data which is learning data with answer labels in a source domain and obtain target learning data which is learning data with answer labels in a target domain, the target learning data including a plurality of first data items each of which is given a first label and a plurality of second data items each of which is given a second label, (b) obtain a plurality of first output vectors by inputting the plurality of first data items to a second neural network and obtain a plurality of second output vectors by inputting the plurality of second data items to the second neural network, the first neural network and the second neural network having identical weighting values, identical numbers of hidden layers, identical numbers of neurons in each of the hidden layers, identical numbers of neurons in an input layer, and identical numbers of neurons in an output layer, and (c) generate a first relation vector corresponding to the first label by using the plurality of first output vectors and generate a second relation vector corresponding to the second label by using the plurality of second output vectors. 12. A non-transitory computer-readable recording medium storing a control program for causing a device including a processor to execute a process, the process causing a first circuit included in an identifying apparatus to (v) obtain a first relation vector and a second relation vector from a learning apparatus including a second circuit, the process causing the second circuit to (a) obtain a first neural network that has learned by using source learning data which is learning data with answer labels in a source domain and obtain target learning data which is learning data with answer labels in a target domain, the target learning data including a plurality of first data items each of which is given a first label and a plurality of second data items each of which is given a second label, (b) obtain a plurality of first output vectors by inputting the plurality of first data items to a second neural network and obtain a plurality of second output vectors by inputting the plurality of second data items to the second neural network, the first neural network and the second neural network having identical weighting values, identical numbers of hidden layers, identical numbers of neurons in each of the hidden layers, identical numbers of neurons in an input layer, and identical numbers of neurons in an output layer, and (c) generate the first relation vector corresponding to the first label by using the plurality of first output vectors and generate the second relation vector corresponding to the second label by using the plurality of second output vectors, and the process causing the first circuit to (w) obtain test data which is data without a label and of a type identical to the target learning data, (x) obtain an output vector for the obtained test data by inputting the test data to the second neural network, (y) calculate a distance between the obtained output vector and the first relation vector and a distance between the obtained output vector and the second relation vector, and (z) output, as an answer label for the test data, the first label or the second label corresponding to one of the first relation vector and the second relation vector that is closer to the output vector. 13. A learning apparatus comprising: at least one memory; and at least one circuit, wherein the circuit (a) obtains a neural network that has learned by using source learning data which is learning data with answer labels in a source domain and obtains target learning data which is learning data with answer labels in a target domain, the target learning data including a plurality of first data items each of which is given a first label and a plurality of second data items each of which is given a second label, (b) obtains a plurality of first output vectors by inputting the plurality of first data items to the neural network and obtains a plurality of second output vectors by inputting the plurality of second data items to the neural network, and (c) generates a first relation vector corresponding to the first label by using the plurality of first output vectors and generates a second relation vector corresponding to the second label by using the plurality of second output vectors. 14. A learning apparatus comprising: a circuit; and a memory, wherein the circuit, in a preparation step, (a-1) obtains structure information indicating a structure of a first neural network and first weighting values used to stipulate relationships between neurons included in the first neural network, the first neural network having learned using first pairs of first data items and first answer labels, the first data items corresponding to the first answer labels, the structure information including a first number of hidden layers and indicating which of the neurons belongs to which layer among the hidden layers, an input layer, and an output layer, (a-2) obtains second pairs of second data items and a second answer label, and third pairs of third data items and a third answer label, the second data items corresponding to the second answer label, the third data items corresponding to the third answer label, (a-3) provides a second neural network having a structure identical to the first neural network and second weighting values used to stipulate relationships between neurons included in the second neural network, the second weighting values being identical to the first weighting values, (a-4) inputs the second data items to the second neural network and the third data items to the second neural network, (a-5) obtains second output vectors and third output vectors from the second neural network, each of the second data items corresponding to one of the second output vectors, each of the third data items corresponding to one of the third output vectors, the second output vectors being x1=[x11, . . . , x1i], . . . , and xn=[xn1, . . . , xni], and the third output vectors being y1=[y11, . . . , y1i], and yn=[yn1, . . . , yni], and (a-6) generates a first relation vector for the second answer label and a second relation vector for the third answer label, the first relation vector being x=[(x11+ . . . +xn1)/n, . . . , (x1i+ . . . +xni)/n], the second relation vector being y=[(y11+ . . . +yn1)/n, . . . , (y1i+ . . . +yni)/n], and wherein the circuit, in a testing step, (b-1) inputs a test data item to the second neural network, (b-2) obtains an output test vector from the second neural network, the test data item corresponding to the output test vector, the output test vector being z=[z11, . . . , z1i], (b-3) calculates a first distance between the first relation vector and the output test vector and a second distance between the second relation vector and the output test output vector, the first distance being [{(x11+ . . . +xn1)/n-z11}2+ . . . +{(x1i+ . . . +xni)/n−z1i)2]1/2, the second distance being [{(y11+ . . . +yn1)/n−z11}2+ . . . +{(y1i+ . . . +yni)/n−z1i)2]1/2, and (b-4) outputs the second answer label when the first distance is smaller than the second distance and outputs the third answer label when the second distance is smaller than the first distance.