Area:
Statistics and Biostatistics (HRP)
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High-probability grants
According to our matching algorithm, Saharon Rosset is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 2005 — 2008 |
Zhu, Ji
Rosset, Saharon |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Flexible Classification and Regression @ University of Michigan Ann Arbor
The research aims to combine statistical and computational considerations
in designing new and useful predictive modeling tools and algorithms.
Specifically, the research involves the development of: a) new
statistically motivated multi-class boosting algorithms, based on a family
of multi-class loss functions and forward stagewise additive modeling; b)
a family of (loss, penalty) pairs that give piecewise linear solution
paths, and yield modeling tools for both regression and classification
which are robust, adaptable and efficient; c) a general theory and
efficient algorithms for solving an L1 regularized problem in infinite
dimensional predictor space.
With the advent of modern technologies, the needs for predictive
modeling tools have been increasing rapidly. Consequently, many new ideas
and methods have been finding their way into the statistical community in
recent years. These are mainly related to the design and analysis of
useful techniques for modeling of high dimensional, noisy data, and these
techniques are now being applied to bioinformatics, high energy physics,
speech recognition, text mining, and a wide range of other important
practical problems. This research aims to push these developments forward
along the line of regularization in predictive modeling, and is expected
to have broader impacts on the practice and education in the domains of
statistics, machine learning and data mining.
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