U****r
发帖数: 206 | 1 David Donoho from Stanford
"for his profound contributions to modern mathematical statistics and in
particular the development of optimal algorithms for statistical estimation
in the presence of noise and of efficient techniques for sparse
representation and recovery in large data-sets."
http://www.shawprize.org/en/shaw.php?tmp=5&twoid=79&threeid=221 |
U****r
发帖数: 206 | 2 可以洗洗睡啦!
★ 发自iPhone App: ChineseWeb 7.8
【在 U****r 的大作中提到】
: David Donoho from Stanford
: "for his profound contributions to modern mathematical statistics and in
: particular the development of optimal algorithms for statistical estimation
: in the presence of noise and of efficient techniques for sparse
: representation and recovery in large data-sets."
: http://www.shawprize.org/en/shaw.php?tmp=5&twoid=79&threeid=221
|
f******h
发帖数: 104 | 3 Candes的老板
estimation
【在 U****r 的大作中提到】
: David Donoho from Stanford
: "for his profound contributions to modern mathematical statistics and in
: particular the development of optimal algorithms for statistical estimation
: in the presence of noise and of efficient techniques for sparse
: representation and recovery in large data-sets."
: http://www.shawprize.org/en/shaw.php?tmp=5&twoid=79&threeid=221
|
X******2
发帖数: 5859 | 4 在统计领域Donoho拿到这个奖基本属于名至实归。
estimation
【在 U****r 的大作中提到】
: David Donoho from Stanford
: "for his profound contributions to modern mathematical statistics and in
: particular the development of optimal algorithms for statistical estimation
: in the presence of noise and of efficient techniques for sparse
: representation and recovery in large data-sets."
: http://www.shawprize.org/en/shaw.php?tmp=5&twoid=79&threeid=221
|
U****r
发帖数: 206 | 5 Donoho在Compressive Sensing贡献不小. |
M****o
发帖数: 4860 | 6 把徒子徒孙都挖去stanford了
牛
【在 f******h 的大作中提到】
: Candes的老板
:
: estimation
|
e**********m
发帖数: 1960 | 7 实至名归
estimation
【在 U****r 的大作中提到】
: David Donoho from Stanford
: "for his profound contributions to modern mathematical statistics and in
: particular the development of optimal algorithms for statistical estimation
: in the presence of noise and of efficient techniques for sparse
: representation and recovery in large data-sets."
: http://www.shawprize.org/en/shaw.php?tmp=5&twoid=79&threeid=221
|
