搜索结果: 1-15 共查到“知识库 相关回归分析”相关记录36条 . 查询时间(0.718 秒)
带特定标记的分位数回归模型
特定标记 分位数 回归模型
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2023/12/13
Quantile regression has become a widely used tool for analysing competing risk data. However, quantile regression for competing risk data with a continuous mark is still scarce. The mark variable is a...
在许多现实的机器学习应用场景中,获取大量未标注的数据是很容易的,但标注过程需要花费大量的时间和经济成本.因此,在这种情况下,需要选择一些最有价值的样本进行标注,从而只利用较少的标注数据就能训练出较好的机器学习模型。
基于逻辑回归函数的加权K-means聚类算法
欧式距离 特征加权的K-means算法 逻辑回归函数 初始聚类中心
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2022/3/15
传统K-means聚类算法通过欧式距离计算样本的相似度，将数据所有的属性特征均平等对待，忽略每个属性特征的不同贡献，导致样本相似度计算的准确率不高。针对这个不足，提出一种特征加权的K-means算法进行优化。首先，运用Softmax和Sigmoid逻辑回归函数计算特征权重，使得加权的欧式距离更能准确地表示样本相似度；其次，优化初始聚类中心选择策略，选择距离较大的K个样本作为初始聚类中心，可有效避免...
股票信息挖掘与LSTM预测
股票预测 长短时记忆神经网络（LSTM） 回归分析
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2022/3/23
由于受到经济环境、政治政策、市场新闻等多种因素的影响，使得预测股票动态变得极具挑战性。研究了5种常用的预测股价变动的预测方法，通过逐步增加模型的输入维度进行预测分析。首先，建立5种优化的预测模型——基于时间序列的自回归平均模型(ARMA)、灰色预测模型(GM(1,1))、BP神经网络模型(BPNN)、基于改进网格寻优算法的支持向量回归(SVR)模型、基于Tensorflow的长短时记忆神经网络模型...
基于符号回归的产品评论数量与购买数量关系分析
购买数量 评论数量 关系模型 符号回归方法
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2022/3/25
为揭示产品评论数量与购买数量间的关系,采用数据驱动的符号回归方法,该方法不需预先假设模型结构,从数据中自动学习出描述产品评论数量与购买数量的关系模型和相关参数。数据采自天猫商城网站中隶属于30种商品的5387个售出产品以及相关评论,利用符号回归方法进行自动学习,发现了在实证方法中假设的线性模型。这种模式在购买数量与评论数量之间的关系上具有更高的适应性。
ADAPTIVE SPLINE ESTIMATES FOR NONPARAMETRIC REGRESSION MODELS
ADAPTIVE SPLINE ESTIMATES NONPARAMETRIC REGRESSION MODELS
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2015/8/25
ADAPTIVE SPLINE ESTIMATES FOR NONPARAMETRIC REGRESSION MODELS.
Least Angle Regression
Least Angle Regression
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2015/8/21
The purpose of model selection algorithms such as All Subsets, Forward Selection, and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be a...
Prediction by Supervised Principal Components
Gene expression Microarray Regression Survival analysis
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2015/8/21
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called ...
Forward stagewise regression and the monotone lasso
regression lasso stagewise
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2015/8/21
We consider the least angle regression and forward stagewise algorithms for solving penalized least squares regression problems. In Efron,Hastie, Johnstone & Tibshirani (2004) it is proved that the le...
PATHWISE COORDINATE OPTIMIZATION
Coordinate descent lasso convex optimization
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2015/8/21
We consider “one-at-a-time” coordinate-wise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the L1-penalized regression (lasso) in the l...
Genomewide Association Analysis by Lasso Penalized Logistic Regression
Genomewide Association Analysis Lasso Penalized Logistic Regression
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2015/8/21
In ordinary regression, imposition of a lasso penalty makes continuous model selection straightforward. Lasso penalized regression is particularly advantageous when the number of predictors far exceed...
Strong Rules for Discarding Predictors in Lasso-type Problems
Strong Rules Discarding Predictors Lasso-type Problems
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2015/8/21
We consider rules for discarding predictors in lasso regression and related problems, for computational efficiency. El Ghaoui et al. (2010) propose “SAFE” rules, based on univariate inner products bet...
Detecting changes in functional linear models
functional data projections weak dependence change point weak convergence
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2011/6/15
We observe two sequences of curve which are connected via an integral operator. Our model includes linear models as well as autoregressive models in Hilbert spaces. We wish to test the null hypothesis...
Confidence Sets in Time--Series Filtering
Confidence Sets Time--Series Filtering
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2011/1/21
The problem of filtering of finite–alphabet stationary ergodic time series is considered. A method for constructing a confidence set for the (unknown) signal is proposed.
The Hannan-Quinn Proposition for Linear Regression
Hannan-Quinn linear regression the law of iterated logarithms strong consistency
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2011/2/23
We consider the variable selection problem in linear regression. Suppose that we have a set
of random variables X1, · · · ,Xm, Y, ǫ such that Y = Pk2 αkXk +ǫ with π ⊆ {1, · · · ,m} a...