IEEE Transactions on Pattern Analysis and Machine Intelligence

A research group at the Osaka Metropolitan University Graduate School of Informatics has developed a new learning method for artificial intelligence that combines classification performance for data with multiple labels with the ability to learn continually from data. Numerical experiments on real-world multi-label data indicate that the new method outperforms conventional approaches. The simplicity of this algorithm makes it easy to integrate it with other algorithms to devise new ones.

Point set registration problems, i.e. finding corresponding points between shapes represented as point sets, are important in a variety of fields, so algorithms have been developed. Here, a new algorithm is discovered for point set registration problems. This algorithm is demonstrated to solve several typical problems remarkably faster than conventional methods and with the highest accuracy. This novel algorithm will be beneficial to a wide variety of technologies including computer graphics, computer vision, authentication etc.