搜索结果: 1-15 共查到“统计学 feature”相关记录16条 . 查询时间(0.024 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：How to capture tourists' search behavior in tourism forecasts？A two-stage feature selection approach
旅游预测 游客 搜索行为 两阶段 特征选择方法
2023/5/16
Search engine data have been widely used and shown to be useful in tourism demand forecasting. However, considering of the vast amounts of search keywords, how to better capture the tourists' attentio...
Feature Screening for Ultrahigh Dimensional Categorical Data with Applications
Feature Screening Pearson’s Chi-Square Test Screening Consisten- cy Search Engine Marketing
2016/1/26
Ultrahigh dimensional data with both categorical responses and categorical covari-ates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable s...
Feature Screening for Ultrahigh Dimensional Categorical Data with Applications
Feature Screening Pearson’s Chi-Square Test Screening Consisten- cy Search Engine Marketing Text Classification Ultrahigh Dimensional Data
2016/1/20
Ultrahigh dimensional data with both categorical responses and categorical covari-ates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable s...
This paper addresses the problem of unsupervised feature learning for text data.Our method is grounded in the principle of minimum description length and uses a dictionary-based compression scheme to ...
Feature Selection Based on Term Frequency and T-Test for Text Categorization
feature selection term frequency t-test text classification
2013/6/14
Much work has been done on feature selection. Existing methods are based on document frequency, such as Chi-Square Statistic, Information Gain etc. However, these methods have two shortcomings: one is...
Greedy Feature Selection for Subspace Clustering
Subspace clustering unions of subspaces hybrid linear models sparse ap-proximation structured sparsity nearest neighbors low-rank approximation
2013/5/2
Unions of subspaces are powerful nonlinear signal models for collections of high-dimensional data. However, existing methods that exploit this structure require that the subspaces the signals of inter...
$l_{2,p}$ Matrix Norm and Its Application in Feature Selection
$l_{2,p}$ Matrix Norm Its Application Feature Selection
2013/5/2
Recently, $l_{2,1}$ matrix norm has been widely applied to many areas such as computer vision, pattern recognition, biological study and etc. As an extension of $l_1$ vector norm, the mixed $l_{2,1}$ ...
Sequential Lasso for feature selection with ultra-high dimensional feature space
extended BIC feature selection selection consistency Sequential Lasso
2011/7/19
We propose a novel approach, Sequential Lasso, for feature selection in linear regression models with ultra-high dimensional feature spaces.
Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces
Diverging number of parameters Feature selection
2011/7/19
In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problem...
Multi-stage Convex Relaxation for Feature Selection
Multi-stage Convex Relaxation Feature Selection
2011/7/5
A number of recent work studied the effectiveness of feature selection using Lasso. It is known that under the restricted isometry properties (RIP), Lasso does not generally lead to the exact recovery...
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...
A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models
feature selection optimistic bias naive Bayes models gene expression data
2009/9/22
For many classication and regression problems, a large number of
features are available for possible use this is typical of DNA microarray data
on gene expression, for example. Often, for computatio...
LASSO, Iterative Feature Selection and the Correlation Selector: Oracle inequalities and numerical performances
Regression estimation statistical learning confidence regions shrinkage and thresholding methods LASSO
2009/9/16
We propose a general family of algorithms for regression estimation with quadratic loss, on the basis of geometrical considerations. These algorithms are able to select relevant functions into a large...
Feature selection in omics prediction problems using cat scores and false non-discovery rate control
Feature selection omics prediction problems cat scores false non-discovery rate control
2010/3/18
We revisit the problem of feature selection in linear discriminant analysis (LDA),
i.e. when features are correlated. First, we introduce a pooled centroids formulation
of the multi-class LDA predic...
“Pre-conditioning” for feature selection and regression in high-dimensional problems
Pre-conditioning feature selection regression high-dimensional problems
2010/4/27
The primary method used for this initial regression is supervised principal components. Then we
apply a standard procedure such as forward stepwise selection or the
LASSO to the pre-conditioned resp...