搜索结果: 1-11 共查到“理论统计学 logistic”相关记录11条 . 查询时间(0.075 秒)
Support Vector Machines,Kernel Logistic Regression,and Boosting
Support Vector Machines Kernel Logistic Regression Boosting
2015/8/21
Support Vector Machines,Kernel Logistic Regression,and Boosting.
Boosting with the Logistic Loss is Consistent
Boosting additive logistic regression coordinate descent convex analysis
2013/6/14
This manuscript provides optimization guarantees, generalization bounds, and statistical consistency results for AdaBoost variants which replace the exponential loss with the logistic and similar loss...
Bayesian Modeling and MCMC Computation in Linear Logistic Regression for Presence-only Data
Bayesian modeling case-control design data augmentation logistic regres-sion Markov Chain Monte Carlo population prevalence presence-only data simulation
2013/6/13
Presence-only data are referred to situations in which, given a censoring mechanism, a binary response can be observed only with respect to on outcome, usually called \textit{presence}. In this work w...
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression
Adaptivity averaged stochastic gradient descent local strong convexity logistic regression
2013/4/28
In this paper, we consider supervised learning problems such as logistic regression and study the stochastic gradient method with averaging, in the usual stochastic approximation setting where observa...
K-Nearest Neighbour algorithm coupled with logistic regression in medical case-based reasoning systems. Application to prediction of access to the renal transplant waiting list in Brittany
Case-based Reasoning systems logistic models similarity measures k-nearest neighbors algorithms classi-fication
2013/4/28
Introduction. Case Based Reasoning (CBR) is an emerg- ing decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of sol...
Matrix Variate Logistic Regression Analysis
Asymptotic theory Logistic regression Matrix variate covariates Regularization Tensor object
2011/6/17
Logistic regression has been widely applied in the field of biostatistics for a long
time. It aims to model the conditional success probability of an event of interest
as the logit function of a lin...
Using Logistic Regression to Analyze the Balance of a Game: The Case of StarCraft II
Balance of a Game Logistic Regression StarCraft II
2011/6/16
Recently, the market size of online game has been increasing astonishingly fast, and so does the
importance of good game design. In online games, usually a human user competes with others,
so the fa...
High-dimensional Ising model selection using ${\ell_1}$-regularized logistic regression
High-dimensional model selection
2010/10/14
We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on $\ell_1$-regularized logistic regression, in which the neighborhood of...
Compressing Parameters in Bayesian High-order Models with Application to Logistic Sequence Models
compressing parameters high-ordermodels Markov chain Monte Carlo logistic models interaction
2009/9/22
Bayesian classication and regression with high-order interactions is
largely infeasible because Markov chain Monte Carlo (MCMC) would need to be
applied with a great many parameters, whose number in...
Sensitivity Analysis to Select the Most Influential Risk Factors in a Logistic Regression Model
Risk Factors Logistic Regression Model
2009/9/3
The traditional variable selection methods for survival data depend on iteration procedures, and control of this process assumes tuning parameters that are problematic and time consuming, especially i...
A weakly informative default prior distribution for logistic and other regression models
Bayesian inference generalized linear model least squares hierarchicalmodel linear regression logistic regression
2010/3/17
We propose a new prior distribution for classical (nonhierarchical)
logistic regression models, constructed by first scaling all nonbinary
variables to have mean 0 and standard deviation 0.5, and th...