A feature transformation learning device includes an approximation unit, a loss calculation unit, an approximation control unit, and a loss control unit. The approximation unit takes a feature value that is extracted from a sample pattern and then weighted by a training parameter, assigns that weighted feature value to a variable of a continuous approximation function approximating a step function, and, by doing so, computes an approximated feature value. The loss calculation unit calculates a loss with respect to the task on the basis of the approximated feature value. The approximation control unit controls an approximation precision of the approximation function with respect to the step function such that the approximation function used with the approximation unit approaches the step function according to a decrease in the loss. The loss control unit updates the training parameter such that the loss decreases.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A feature transformation learning device comprising: an approximation unit that calculates an approximate feature by substituting a weighted feature in a variable of an approximation function that is continuous and approximates a step function, the weighted feature being a feature that is extracted from a sample pattern and is weighted by a learning object parameter; a loss calculation unit that calculates a loss to a task based on the approximate feature; an approximation control unit that controls approximation accuracy of the approximation function to the step function in such a way that the approximation function used in the approximation unit becomes closer to the step function with a decrease in the loss; and a loss control unit that updates the learning object parameter so as to decrease the loss. 2. The feature transformation learning device according to claim 1, wherein the loss control unit calculates a parameter that minimizes an object function, and updates the learning object parameter with the parameter thus calculated, the object function being a function in which a value of a function whose value becomes smaller with an increase of an absolute value of the approximate feature is added to the loss. 3. The feature transformation learning device according to claim 1, wherein the approximation function includes, as an approximation accuracy parameter, the function that changes the approximation accuracy and the approximation control unit controls the approximation accuracy of the approximation function by changing the approximation accuracy parameter in a direction in which the approximation accuracy increases with the update of the learning object parameter. 4. The feature transformation learning device according to claim 1, further comprising, an extraction unit that extracts the feature from the sample pattern. 5. A feature transformation learning method, comprising: calculating an approximate feature by substituting a weighted feature in a variable of an approximation function that is continuous and approximates a step function, the weighted feature being a feature that is extracted from a sample pattern and is weighted by a learning object parameter; calculating a loss to a task based on the approximate feature; controlling approximation accuracy of the approximation function to the step function in such a way that the approximation function becomes closer to the step function with a decrease in the loss; and updating the learning object parameter so as to decrease the loss. 6. A non-transitory computer-readable recording medium storing a computer program that causes a computer to perform a set of processes, the set of processes comprising: a process to calculate an approximate feature by substituting a weighted feature in a variable of an approximation function that is continuous and approximates a step function, the weighted feature being a feature that is extracted from a sample pattern and is weighted by a learning object parameter; a process to calculate a loss to a task based on the approximate feature; a process to control approximation accuracy of the approximation function to the step function in such a way that the approximation function becomes closer to the step function with a decrease in the loss; and a process to update the learning object parameter so as to decrease the loss. 7. A feature transformation learning device comprising: approximation means for calculating an approximate feature by substituting a weighted feature in a variable of an approximation function that is continuous and approximates a step function, the weighted feature being a feature that is extracted from a sample pattern and is weighted by a learning object parameter; loss calculation means for calculating a loss to a task based on the approximate feature; approximation control means for controlling approximation accuracy of the approximation function to the step function in such a way that the approximation function used in the approximation means becomes closer to the step function with a decrease in the loss; and loss control means for updating the learning object parameter so as to decrease the loss.