BACKGROUND

The present invention relates generally to the field of machine learning, and more particularly to applying squeezed space model to later training data.

Training a neural network with a small dataset (or large dataset) can cause the network to memorize all training examples, in turn leading to overfitting/underfitting and poor performance on a dataset. Small or large datasets may also represent a harder mapping problem for neural networks to learn, given the sparse sampling of points in the input space. One approach to making the input space smoother and easier to learn is to add noise to inputs during training.

The most common type of noise used during training is the addition of Gaussian noise to input variables. Gaussian noise, or white noise, can be generated as needed using a pseudorandom number generator. The amount of noise added is a user configurable parameter. Too little noise has no effect, whereas too much noise makes the mapping function too challenging to learn by the neural network.

SUMMARY

Aspects of the present invention disclose a method, computer program product, and system for training a neural network based on introducing noise. The method includes modeling environmental noise and internal system noise with a first and second polynomial regression to generate an upper bound function and a lower bound function, respectively; modeling user noise with a third polynomial regression to generate a noise function; applying the upper bound function and the lower bound function to the noise function to generate a squeezed noise function; detecting anomalies within a training data during an online training; generating sample values from the detected anomalies; mapping the sample values to a squeezed space model via the squeezed noise function to calculate an average noise index within the sampling space bounded by the squeezed noise function; and applying the average noise index to the training data during training to alter the training data, emulating a noisy environment.

In another embodiment, the computer program product includes one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising: program instructions to model environmental noise and internal system noise with a first and second polynomial regression to generate an upper bound function and a lower bound function, respectively; program instructions to model user noise with a third polynomial regression to generate a noise function; program instructions to apply the upper bound function and the lower bound function to the noise function to generate a squeezed noise function; program instructions to detect anomalies within a training data during an online training; program instructions to generate sample values from the detected anomalies; program instructions to map the sample values to a squeezed space model via the squeezed noise function to calculate an average noise index within the sampling space bounded by the squeezed noise function; and program instructions to apply the average noise index to the training data during training to alter the training data, emulating a noisy environment.

In yet another embodiment, the computer system includes one or more computer processors; one or more computer readable storage media; program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising: program instructions to model environmental noise and internal system noise with a first and second polynomial regression to generate an upper bound function and a lower bound function, respectively; program instructions to model user noise with a third polynomial regression to generate a noise function; program instructions to apply the upper bound function and the lower bound function to the noise function to generate a squeezed noise function; program instructions to detect anomalies within a training data during an online training; program instructions to generate sample values from the detected anomalies; program instructions to map the sample values to a squeezed space model via the squeezed noise function to calculate an average noise index within the sampling space bounded by the squeezed noise function; and program instructions to apply the average noise index to the training data during training to alter the training data, emulating a noisy environment.

