全文获取类型
收费全文 | 289篇 |
免费 | 7篇 |
国内免费 | 9篇 |
专业分类
化学 | 182篇 |
力学 | 7篇 |
数学 | 73篇 |
物理学 | 43篇 |
出版年
2023年 | 2篇 |
2022年 | 2篇 |
2021年 | 4篇 |
2019年 | 3篇 |
2017年 | 5篇 |
2016年 | 5篇 |
2015年 | 5篇 |
2014年 | 6篇 |
2013年 | 17篇 |
2012年 | 11篇 |
2011年 | 12篇 |
2010年 | 7篇 |
2009年 | 15篇 |
2008年 | 19篇 |
2007年 | 25篇 |
2006年 | 21篇 |
2005年 | 18篇 |
2004年 | 11篇 |
2003年 | 10篇 |
2002年 | 9篇 |
2001年 | 4篇 |
2000年 | 8篇 |
1999年 | 6篇 |
1998年 | 2篇 |
1997年 | 3篇 |
1995年 | 4篇 |
1994年 | 2篇 |
1993年 | 8篇 |
1990年 | 3篇 |
1989年 | 1篇 |
1988年 | 2篇 |
1987年 | 8篇 |
1984年 | 1篇 |
1982年 | 4篇 |
1980年 | 2篇 |
1979年 | 5篇 |
1978年 | 5篇 |
1977年 | 3篇 |
1976年 | 3篇 |
1975年 | 2篇 |
1974年 | 5篇 |
1973年 | 3篇 |
1971年 | 2篇 |
1970年 | 1篇 |
1969年 | 1篇 |
1967年 | 1篇 |
1965年 | 1篇 |
1964年 | 1篇 |
1963年 | 1篇 |
1961年 | 2篇 |
排序方式: 共有305条查询结果,搜索用时 93 毫秒
1.
2.
Ben Brown Christopher J. Miller Julian Wolfson 《Journal of computational and graphical statistics》2017,26(3):579-588
Most variable selection techniques for high-dimensional models are designed to be used in settings, where observations are independent and completely observed. At the same time, there is a rich literature on approaches to estimation of low-dimensional parameters in the presence of correlation, missingness, measurement error, selection bias, and other characteristics of real data. In this article, we present ThrEEBoost (Thresholded EEBoost), a general-purpose variable selection technique which can accommodate such problem characteristics by replacing the gradient of the loss by an estimating function. ThrEEBoost generalizes the previously proposed EEBoost algorithm (Wolfson 2011) by allowing the number of regression coefficients updated at each step to be controlled by a thresholding parameter. Different thresholding parameter values yield different variable selection paths, greatly diversifying the set of models that can be explored; the optimal degree of thresholding can be chosen by cross-validation. ThrEEBoost was evaluated using simulation studies to assess the effects of different threshold values on prediction error, sensitivity, specificity, and the number of iterations to identify minimum prediction error under both sparse and nonsparse true models with correlated continuous outcomes. We show that when the true model is sparse, ThrEEBoost achieves similar prediction error to EEBoost while requiring fewer iterations to locate the set of coefficients yielding the minimum error. When the true model is less sparse, ThrEEBoost has lower prediction error than EEBoost and also finds the point yielding the minimum error more quickly. The technique is illustrated by applying it to the problem of identifying predictors of weight change in a longitudinal nutrition study. Supplementary materials are available online. 相似文献
3.
4.
5.
6.
7.
Qingying Bu Gerard Buskes Alexey I. Popov Adi Tcaciuc Vladimir G. Troitsky 《Positivity》2013,17(2):283-298
We investigate the relationship between the diagonal of the Fremlin projective tensor product of a Banach lattice E with itself and the 2-concavification of E. 相似文献
8.
9.
The Soreq Applied Research Accelerator Facility (SARAF): Overview,research programs and future plans
Israel Mardor Ofer Aviv Marilena Avrigeanu Dan Berkovits Adi Dahan Timo Dickel Ilan Eliyahu Moshe Gai Inbal Gavish-Segev Shlomi Halfon Michael Hass Tsviki Hirsh Boaz Kaiser Daniel Kijel Arik Kreisel Yonatan Mishnayot Ish Mukul Ben Ohayon Michael Paul Amichay Perry Hitesh Rahangdale Jacob Rodnizki Guy Ron Revital Sasson-Zukran Asher Shor Ido Silverman Moshe Tessler Sergey Vaintraub Leo Weissman 《The European Physical Journal A - Hadrons and Nuclei》2018,54(5):91
10.