منابع مشابه
Electrical dimension of the nuclear envelope.
Eukaryotic chromosomes are confined to the nucleus, which is separated from the rest of the cell by two concentric membranes known as the nuclear envelope (NE). The NE is punctuated by holes known as nuclear pore complexes (NPCs), which provide the main pathway for transport of cellular material across the nuclear-cytoplasmic boundary. The single NPC is a complicated octameric structure contain...
متن کاملModel averaging and dimension selection for the singular value decomposition
Many multivariate data analysis techniques for an m× n matrix Y are related to the model Y = M + E, where Y is an m × n matrix of full rank and M is an unobserved mean matrix of rank K < (m ∧ n). Typically the rank of M is estimated in a heuristic way and then the least-squares estimate of M is obtained via the singular value decomposition of Y, yielding an estimate that can have a very high va...
متن کاملModel selection for partial least squares based dimension reduction
0167-8655/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.patrec.2011.11.009 ⇑ Corresponding author. Address: Department of C Tongji University, Cao’an Road 4800, Shanghai 2018 3706; fax: +86 21 6958 9241. E-mail address: [email protected] (M. You). Partial least squares (PLS) has been widely applied to process scientific data sets as an effective dimension reduction technique. The main...
متن کاملParameter-free online learning via model selection
We introduce an efficient algorithmic framework for model selection in online learning, also known as parameter-free online learning. Departing from previous work, which has focused on highly structured function classes such as nested balls in Hilbert space, we propose a generic meta-algorithm framework that achieves online model selection oracle inequalities under minimal structural assumption...
متن کاملDimension-Free Exponentiated Gradient
I present a new online learning algorithm that extends the exponentiated gradient framework to infinite dimensional spaces. My analysis shows that the algorithm is implicitly able to estimate the L2 norm of the unknown competitor, U , achieving a regret bound of the order of O(U log(U T + 1)) √ T ), instead of the standard O((U + 1) √ T ), achievable without knowing U . For this analysis, I int...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2018
ISSN: 1935-7524
DOI: 10.1214/18-ejs1449