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.
Created:10/24/12Dr Akira Saito to give keynote address at SPIE Medical Imaging 2013
Dr Akira Saito, head of the BioMedical Imaging and Informatics Group in the Medical Solutions Division will give the keynote address at the Digital Pathology conference during the SPIE Medical Imaging symposium to be held in DisneyWorld resort in Florida, February 14 2013. Dr Saito will touch on the latest developments in digital pathology and the e-Pathologist system currently commercialized by NEC in Japan. He will also describe the current collaborations of the group with academic and medical institutions in Japan.
Created:01/04/122012 Benjamin Franklin Medal
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."
Created:01/03/122012 Frank Rosenblatt Award
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."
Large Scale Transduction
Transduction and semi-supervised learning methods can help
improve generalization ability in learning problems through
the use of the test labels, or unlabeled data, during learning.
algorithms are unfeasibly slow. We investigate how to make
large scale algorithms in this domain.
MiLDe is an integrated development environment with a
suite of machine learning tools
including SVMs, Neural Networks (using the Torch package),
image analysis, numeric functions and dataset handling.
Automated gastric cancer diagnosis on H&E-stained sections; large scale training with multiple instance machine learning
. SPIE Medical Imaging: Digital Pathology
Efficient sequence kernel-based genome-wide prediction of transcription factors
Learning the Dependency Structure of Latent Factors
. Advances in Neural Information Processing Systems (NIPS 2012)
A Binary Classification Framework for Two Stage Multiple Kernel Learning
. Proceedings of the International Conference on Machine Learning
2D similarity kernels for biological sequence classification
Mitotic figure recognition: Agreement among pathologists and computerized detector
. Analytical Cellular Pathology
35(2) (2012) (link
Generalized Similarity Kernels for Efficient Sequence Classification
Using string kernels to predict gene expression
. Snowbird Learning Workshop