LEARNING DEVICE, LEARNING METHOD AND LEARNING PROGRAM
A learning device includes processing circuitry configured to acquire data with a label to be predicted, and learn a model that represents probability distribution of the label of the acquired data using an eigenvector corresponding to a maximum eigenvalue in a Fisher information matrix for the data in the model.
1 . A learning device comprising:
processing circuitry configured to:
acquire data with a label to be predicted; and
learn a model that represents probability distribution of the label of the acquired data using an eigenvector corresponding to a maximum eigenvalue in a Fisher information matrix for the data in the model.
2 . The learning device according to claim 1 , wherein the processing circuitry is further configured to use the eigenvector as an initial value of noise to be added to the data in a loss function.
3 . The learning device according to claim 1 , wherein the processing circuitry is further configured to predict the label of the acquired data using the learned model.
4 . A learning method executed by a learning device comprising:
acquiring data with a label to be predicted; and
learning a model that represents probability distribution of the label of the acquired data using an eigenvector corresponding to a maximum eigenvalue in a Fisher information matrix for the data in the model.
5 . A non-transitory computer-readable recording medium storing therein a learning program that causes a computer to execute a process comprising:
acquiring data with a label to be predicted; and
learning a model that represents probability distribution of the label of the acquired data using an eigenvector corresponding to a maximum eigenvalue in a Fisher information matrix for the data in the model.