搜索结果: 16-30 共查到“统计学 optimization”相关记录30条 . 查询时间(0.19 秒)
On the consistency of AUC Optimization
AUC consistency surrogate loss cost-sensitive learning learning to rank
2012/9/18
AUC (area under ROC curve) is an important evaluation criterion, which has been popularly used in diverse learning tasks such as class-imbalance learning, cost-sensitive learning, learning to rank and...
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions
Stochastic optimization sparse statistical recovery optimal algorithm high dimensions
2012/9/19
We develop and analyze stochastic optimization algorithms for problems in which the ex-pected loss is strongly convex, and the optimum is (approximately)sparse. Previous approaches are able to exploit...
An estimation of distribution algorithm with adaptive Gibbs sampling for unconstrained global optimization
Estimation of distribution algorithms Evolutionary algorithms
2011/7/19
In this paper is proposed a new heuristic approach belonging to the field of evolutionary Estimation of Distribution Algorithms (EDAs). EDAs builds a probability model and a set of solutions is sample...
A General Framework for Structured Sparsity via Proximal Optimization
General Framework Structured Sparsity Proximal Optimization
2011/7/7
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimi...
All-at-once Optimization for Coupled Matrix and Tensor Factorizations
data fusion matrix factorizations tensor factorizations CANDECOMP PARAFAC missing data
2011/6/21
Joint analysis of data from multiple sources has the potential
to improve our understanding of the underlying structures
in complex data sets. For instance, in restaurant recommendation
systems, re...
Generalized Boosting Algorithms for Convex Optimization
Generalized Boosting Algorithms Convex Optimization
2011/6/21
Boosting is a popular way to derive power-
ful learners from simpler hypothesis classes.
Following previous work (Mason et al., 1999;
Friedman, 2000) on general boosting frame-
works, we analyze g...
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span of only a few columns ...
Exact block-wise optimization in group lasso for linear regression
Block coordinate descent convex optimization group LASSO sparse group LASSO
2010/10/19
The group lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level. Existing methods for finding the ...
Multivariate quantiles and multiple-output regression quantiles：From L1 optimization to halfspace depth
Multivariate quantile quantile regression halfspace depth
2010/3/10
A new multivariate concept of quantile, based on a directional
version of Koenker and Bassett’s traditional regression quantiles, is
introduced for multivariate location and multiple-output regressi...
Discussion of “Multivariate quantiles and multiple-output regression quantiles：From L1 optimization to halfspace depth”
Multivariate quantiles multiple-output regression quantiles L1 optimization halfspace depth
2010/3/10
First I would like to congratulate the authors for developing a new concept
of directional quantile contours. The work will contribute well to the pursuit
of multivariate quantiles. The multiple out...
Feature Extraction for Universal Hypothesis Testing via Rank-constrained Optimization
Universal test mismatched universal test hypothesistesting feature extraction exponential family
2010/3/9
This paper concerns the construction of universal
tests for binary hypothesis testing, in which the alternate hypothesis
is poorly modeled and the observation space is large.
The mismatched univers...
Snell's optimization problem for sequences of convex compact valued random sets
Snell's optimization problem sequences of convex valued random sets
2009/9/21
A random set analogue of the Snell problem is presented.
In the original Snell's problem one observes a sequence of random
variables (t,), say a gambler's capital at successive games. If the gambler...
Portfolio Optimization with Non-Constant Volatility and Partial Information
Portfolio Optimization Non-Constant Volatility Partial Information
2009/9/17
Portfolio Optimization with Non-Constant Volatility and Partial Information。
Optimization of touristic distribution networks using genetic algorithms
Distribution networks vehicle routing problem tourism demand air transportation genetic algorithms edge mapped recombination operator
2009/2/23
The eight basic elements to design genetic algorithms (GA) are described and applied to
solve a low demand distribution problem of passengers for a hub airport in Alicante and 30
touristic destinati...
Best subset selection，persistence in high-dimensional statistical learning and optimization under L1 constraint
Variable selection persistence
2010/4/26
Let (Y,X1, . . . ,Xm) be a random vector. It is desired to predict Y
based on (X1, . . . ,Xm). Examples of prediction methods are regression,
classification using logistic regression or separating h...