A method for learning a classification model by a computer system is disclosed. One or more positive class data and one or more negative class data are prepared. The classification model is trained based on the positive class data to adjust one or more parameters of the classification model so that the positive class data is reconstructed by the classification model. The classification model is trained based on the negative class data to adjust the one or more parameters so that the negative class data is prevented from being reconstructed by the classification model. For the negative class data, changes in the one or more parameters with gradient of an objective function may be calculated using an unsupervised learning algorithm. The one or more parameters may be updated based on the changes in an opposite manner to the training based on the positive class.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for learning a classification model by a computer system, the method comprising: preparing one or more positive class data and one or more negative class data; training the classification model based on the positive class data to adjust one or more parameters of the classification model so that the positive class data is reconstructed by the classification model; and training the classification model based on the negative class data to adjust the one or more parameters so that the negative class data is prevented from being reconstructed by the classification model. 2. The method of claim 1, wherein the training based on the negative class data comprises: calculating changes in the one or more parameters with gradient of an objective function using an unsupervised learning algorithm; and updating the one or more parameters based on the changes in an opposite manner to the training based on the positive class. 3. The method of claim 2, wherein the unsupervised learning algorithm is any one of unsupervised learning algorithms including a contrastive divergence method, a stochastic gradient descent method, a batch gradient descent method, a persistent contrastive divergence method, a Gibbs sampling method or variants thereof. 4. The method of claim 1, wherein iteration for the training based on the negative class data is repeated before the training based on the positive class data is performed. 5. The method of claim 1, wherein the training based on the positive class data and the training based on the negative class data are alternately performed until a predetermined criterion is satisfied and the learning for reconstruction is finished at the training based on the positive class data. 6. The method of claim 1, wherein the classification model includes at least parameter used for reconstructing or encoding a signal. 7. The method of claim 1, wherein the classification model is an autoencoder or includes at least part of an autoencoder. 8. The method of claim 1, wherein the trained classification model encodes an input signal similar to any one of the negative class data and decodes it to the closest representation that is similar to the positive class data. 9. A method for learning a classification model by a computer system, the method comprising: preparing one or more positive class data and one or more negative class data; training the classification model based on the positive class data to adjust one or more parameters of the classification model; and training the classification model based on the negative class data to adjust the one or more parameters in an opposite manner to the training based on the positive class data in regard to gradient of an objective function. 10. The method of claim 9, wherein the training based on the negative class data comprises: updating the one or more parameters against the gradient of the objective function. 11. A method for learning a neural network by a computer system, the method comprising: preparing one or more positive class data and one or more negative class data; training the neural network based on the positive class data such that connections used to reconstruct the positive class data become strong; and training the neural network based on the negative class data such that connections used to reconstruct the negative class data become weak. 12. A computer system for learning a classification model by executing program instructions, the computer system comprising: a memory tangibly stores the program instructions; and a processor in communications with the memory, wherein the computer system is configured to: prepare one or more positive class data and one or more negative class data; train the classification model based on the positive class data to adjust one or more parameters of the classification model so that the positive class data is reconstructed by the classification model; and train the classification model based on the negative class data to adjust the one or more parameters so that the negative class data is prevented from being reconstructed by the classification model. 13. The computer system of claim 12, wherein the computer system is further configured to: calculate changes in the one or more parameters with gradient of an objective function by using an unsupervised learning algorithm to train based on the negative class data; and update the one or more parameters based on the changes in an opposite manner for the positive class to train based on the negative class data. 14. The computer system of claim 13, wherein the unsupervised learning algorithm is any one of unsupervised learning algorithms including a contrastive divergence method, a stochastic gradient descent method, a batch gradient descent method, a persistent contrastive divergence method, a Gibbs sampling method or variants thereof. 15. The computer system of claim 12, wherein the computer system is further configured to: repeating iteration for the negative class data before the training based on the positive class data is performed. 16. The computer system of claim 12, wherein the computer system is further configured to: train the classification model based on the positive class data and the negative class data alternately; and train the classification model trained based on the positive class data at end of the learning for reconstruction. 17. The computer system of claim 12, wherein the classification model is a model including at least parameters used for reconstruction or encoding a signal. 18. The computer system of claim 12, wherein the trained classification model encodes an input signal similar to any one of the negative class data and decodes it to the closest representation that is similar to the positive class data. 19. The computer system of claim 12, wherein the computer system is provided in a cloud computing environment. 20. A computer program product for learning a classification model, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising: preparing one or more positive class data and one or more negative class data; training the classification model based on the positive class data to adjust one or more parameters of the classification model so that the positive class data is reconstructed by the classification model; and training the classification model based on the negative class data to adjust the one or more parameters so that the negative class data is prevented from being reconstructed by the classification model. 21. The computer program product of claim 20, wherein the training based on the negative class data comprises: calculating changes in the one or more parameters with gradient of an objective function using an unsupervised learning algorithm; and updating the one or more parameters based on the changes in an opposite manner to the training based on the positive class. 22. The computer program product of claim 21, wherein the unsupervised learning algorithm is any one of unsupervised learning algorithms including a contrastive divergence method, a stochastic gradient descent method, a batch gradient descent method, a persistent contrastive divergence method, a Gibbs sampling method or variants thereof. 23. The computer program product of claim 20, wherein iteration for the training based on the negative class data is repeated before the training based on the positive class data is performed. 24. The computer program product of claim 20, wherein the training based on the positive class data and the training based on the negative class data are alternately performed until satisfying a predetermined criterion, and the learning for reconstruction is finished at the training based on the positive class data. 25. The computer program product of claim 20, wherein the classification model is a model including at least parameters used for reconstructing or encoding a signal.