An apparatus for classifying data using a neural network includes an input layer configured to receive input data; an output value to output a plurality of first output values of the input data with respect to each of at least one of all classes, and output only one first output value of the input data with respect to each of a rest of all of the classes; and a boost pooling layer to receive one or more output values output for each class, and output one second output value for each class.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus for classifying data into classes using a neural network, the apparatus comprising: an input layer configured to receive input data; an output layer configured to output a plurality of first output values of the input data with respect to each of at least one of the classes, and output only one first output value of the input data with respect to each of a rest of the classes; and a boost pooling layer configured to receive one or more first output values output for each class, and output one second output value for each class. 2. The apparatus of claim 1, wherein the output layer comprises a number of output nodes for each class equal to a number of the first output values output for each class. 3. The apparatus of claim 2, wherein the at least one of the classes with respect to which the plurality of the first output values is to be output, and a number of the first output values to be output with respect to each class of the at least one of the classes, are determined based on a result of comparing a loss function value for each of the classes calculated using the neural network trained using training data, and a preset threshold. 4. The apparatus of claim 2, wherein the boost pooling layer comprises one boost pooling node for each class connected to each output node of a corresponding class of the output layer. 5. The apparatus of claim 4, wherein each boost pooling node is configured to output any one or any combination of any two or more of a maximum, a mean, an average, and a probabilistic selection of the first output values of the output nodes of the corresponding class to which the boost pooling node is connected. 6. The apparatus of claim 1, wherein the neural network further comprises one or more hidden layers between the input layer and the output layer. 7. The apparatus of claim 6, wherein the output layer comprises class groups each comprising an output node for each of the classes; and one hidden layer of the one or more hidden layers is positioned before the output layer, and comprises two or more sets of hidden nodes, each set of which is connected to a different one of the class groups of the output layer. 8. The apparatus of claim 1, further comprising a classifier configured to input the input data to the input layer, receive the one second output value for each class from the boost pooling layer, and determine which class the input data belongs based on the received one second output value for each class. 9. A neural network training method for an apparatus for classifying data, the neural network training method comprising: training a first neural network comprising an input layer and an output layer using training data; calculating a first loss function value for each of a plurality of classes using the trained first neural network; configuring a second neural network by changing the first neural network; training the second neural network using the training data; calculating a second loss function value for each class using the trained second neural network; and determining whether the second neural network is to be reconfigured using either one or both of the first loss function value and the second loss function value. 10. The neural network training method of claim 9, wherein the configuring of the second neural network comprises: adding a boost pooling layer comprising one boost pooling node for each class after the output layer; determining a class for which a plurality of output values are to be output at the output layer; and adding an output node for the determined class to the output layer. 11. The neural network training method of claim 10, wherein the configuring of the second neural network further comprises: connecting the added output node to the boost pooling node for a corresponding class; and setting a weight value of the added output node. 12. The neural network training method of claim 11, wherein the training of the second neural network comprises learning and setting weight values for all nodes of the second neural network using all of the training data. 13. The neural network training method of claim 11, wherein the training of the second neural network comprises learning and setting weight values for all nodes of the output layer using all of the training data. 14. The neural network training method of claim 11, wherein the training of the second neural network comprises learning the weight value to be set for the added output node of the output layer using a part of the training data selected based on the first loss function value. 15. The neural network training method of claim 11, wherein the training of the first neural network comprises learning and setting weight values of nodes of the output layer using the training data; and the setting of the weight value of the added output node comprises setting the weight value of the added output node based on the weight value of another output node of the output layer for a same class. 16. The neural network training method of claim 10, wherein the determining of a class for which a plurality of output values are to be output comprises determining a class in which the calculated first loss function value is greater than a threshold to be the class for which the plurality of output values are to be output. 17. The neural network training method of claim 10, wherein each boost pooling node is configured to output one output value for a corresponding class by applying any one or any combination of any two or more of a maximum, a mean, an average, and a probabilistic selection to all output values of all output nodes of the output layer for the corresponding class. 18. The neural network training method of claim 9, wherein the determining of whether the second neural network is to be reconfigured comprises reconfiguring the second neural network in response to the second loss function value being greater than a threshold, or in response to a difference between the first loss function value and the second loss function value being greater than a threshold.