The NEC machine learning department studies theory and applications of statistical learning theory. We aim to understand learning through the analysis of new induction principles, efficient implementations of those principles, and the development of innovative applications that showcase their performance.

Machine Learning Theory

We aim to advance the theoretical understanding of learning through the analysis of new induction principles and through a paradigm shift from traditional inductive inference to non-inductive inference such as transductive and selective inference.

Fast Learning

Accelerating computation by orders of magnitude through the development of more efficient learning algorithms, and through more efficient implementations.

Algorithms & Applications

Applications of our theoretical and algorithmic advances to innovative applications, showcasing their performance.

 

Featured research projects

Recent News

Vladimir is honored with the NEC C&C Foundation Award 2013

This year's annual award of the NEC C&C Foundation went to Vladimir Vapnik. The citation reads: For Contributions to Establishing Statistical Learning Theory and for the Invention of High-Performance and Practical Learning Algorithms. The NEC C&C Foundation award is one of the highest honors for scientists and engineers in Japan and comes with a prize of JPY 10 Million. Congratulations, Vladimir!! Remark: The NEC C&C Foundation is independent of the NEC corporation.

Dr Akira Saito presented keynote address at SPIE Medical Imaging 2013

Dr Akira Saito, head of the BioMedical Imaging and Informatics Group in the Medical Solutions Division gave the keynote address at the Digital Pathology conference during the SPIE Medical Imaging symposium, February 14 2013. Dr Saito described the latest developments in digital pathology and the e-Pathologist system currently commercialized by NEC in Japan. He also described the current collaborations of the group with academic and medical institutions in Japan.

2012 Benjamin Franklin Medal

Vladimir Vapnik is the recipient of The 2012 Benjamin Franklin Medal in Computer and Cognitive Science with the following citation: "For his fundamental contributions to our understanding of machine learning, which allows computers to classify new data based on statistical models derived from earlier examples, and for his invention of widely used machine learning techniques."


2012 Frank Rosenblatt Award

Vladimir Vapnik has been named recipient of the 2012 IEEE Frank Rosenblatt Award with the following citation: "For development of support vector machines and statistical learning theory as a foundation of biologically inspired learning." Congratulations Vladimir!