Automatic Learning of High Accurate Prediction Models from Limited Supervised Data

Systems and services equipped with sophisticated prediction functions based on machine learning are rapidly expanding, attracting substantial interest. But did you know that there exists a huge barrier of training data development to the introduction of machine learning, particularly deep learning? Currently, deep learning is successfully introduced, primarily in the area of “supervised learning.” However, there is a need to prepare massive amounts of “training data” (data with correct answers) to achieve high prediction accuracy using the supervised learning. In numerous applications, sufficient training data cannot be prepared when attempts are made to introduce new machine learning processes, which may exclude developers and users from the benefits of training data. Even if the data required for building training data were available, the preparation process would require enormous cost and specialized knowledge, thereby posing as a major obstacle to its introduction. The construction of a system that allows the introduction of machine learning even with limited information and the reduction of costs for creating high-quality training data are therefore the foremost challenges in the universal use of intelligent systems in practice. To solve the challenge, this research aims to establish underlying technology for automatically building high precision prediction models from limited training data. The technology will fundamentally solve diverse problems related to the developing of training data, which has posed as a barrier to the introduction of machine learning, from areas where machine learning could not be introduced due to the failure to gather large amounts of high-quality training data to ones where introduction of machine learning had to be given up due to human costs and lack of specialized knowledge required for the development of training data.

SoftBank