搜索结果: 1-6 共查到“管理学 Information Theoretic”相关记录6条 . 查询时间(0.187 秒)
Information-Theoretic Measures of Influence Based on Content Dynamics
entropy link prediction causality social networks
2012/9/18
The fundamental building block of social in uence is for one person to elicit a response in another. Researchers measur-ing a \response" in social media typically depend either on detailed models of h...
Information-theoretic Dictionary Learning for Image Classification
Dictionary learning information theory mutual Dictionary learning information theory mutual
2012/9/18
We present a two-stage approach for learning dic-tionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, d...
A CLT for Information-theoretic statistics of Non-centered Gram random matrices
Random Matrix Spectral measure Stieltjes Transform
2011/7/19
In this article, we study the fluctuations of the random variable: $$ {\mathcal I}_n(\rho) = \frac 1N \log\det(\Sigma_n \Sigma_n^* + \rho I_N),\quad (\rho>0) $$ where $\Sigma_n= n^{-1/2} D_n^{1/2} X_n...
Information theoretic interpretation of frequency domain connectivity measures
Statistics Theory (math.ST) Neurons and Cognition (q-bio.NC)
2010/12/17
To provide adequate multivariate measures of information flow between neural structures, modified expressions of Partial Directed Coherence (PDC) and Directed Transfer Function (DTF), two popular mult...
The M-G-1 retrial queue:An information theoretic approach
Principle of maximum entropy M-G-1 retrial queue limiting distribution busy period waiting time
2009/2/23
In this paper, we give a survey of the use of information theoretic techniques for the estimation of the main performance characteristics of the M/G/1 retrial queue. We focus on the limiting distribut...
Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting
High-dimensional statistical inference subset selection signal denoising compressivesensing model selection
2010/4/26
The problem of recovering the sparsity pattern of a fixed but unknown vector β ∈ Rp
based on a set of n noisy observations arises in a variety of settings, including subset selection in regression, ...