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."
Mass Spectroscopy Analysis
Mass spectrometry, a core technology in the field of proteomics, is commonly
used in a high-throughput fashion to identify proteins in a mixture. Currently,
the primary bottleneck in this type of experiment is computational. Existing algorithms
for interpreting mass spectra are slow and fail to identify a large proportion of given spectra.
SENNA is a fast neural-network architecture for semantic extraction from text.
Automated gastric cancer diagnosis on H&E-stained sections; large scale training with multiple instance machine learning
. SPIE Medical Imaging: Digital Pathology
Classification of mitotic figures with convolutional neural networks and seeded blob features
. J. Pathol. Inform.
4(9) (2013) (link
Biological Sequence Analysis with Multivariate String Kernels
. IEEE/ACM Transactions on Computational Biology and Bioinformatics
Answer Extraction by Recursive Parse Tree Descent
. ACL Workshop on Continuous Vector Space Models and their Compositionality
Efficient sequence kernel-based genome-wide prediction of transcription factors
Efficient time series classification with Multivariate similarity kernels
. NYAS Machine Learning Symposium
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