最小二乘核集成学习

发布时间：2021-12-23 10:46

　　近年来,机器学习取得了飞速的发展。由于最小二乘法在问题制定和实施上的简单性,在过去几年中受到了广泛的关注。尽管最小二乘模型在分类和回归方面具有良好的性能,但它对参数设置很敏感。这一挑战使研究人员更加关注这类单一的模型方法。解决这类问题的有效途径就是引入集成模型。本文首先概述了研究背景和最小二乘法的应用领域。之后简要讨论了研究现状,并介绍了最小二乘法的最后预处理方面。在目前的研究过程中,核集成学习在其应用方面取得了显著的进展。这主要通过提出正则化核集成回归、耦合最小二乘支持向量集成机和样本诱导因子核集成回归来实现的,主要内容如下:1)提出了联合正则核集成回归方案。该方案将多个核回归器同时应用到一个统一的集成回归框架中,并通过最小化核希尔伯特空间中的总集成损失函数来实现共同正则化。通过这种方式,一个对数据进行更精确拟合的核回归器可以自动获得更大的权重,从而获得更好的整体集成性能。与梯度增强法、回归树法、支持向量回归法、岭回归法、随机森林法等一些单模型和集成回归方法相比,我们提出的方法可以在在多个UCI数据集上实现回归和分类问题的最佳性能。2)提出了一种新的基于耦合最小二乘的集成支持向量机（... 

【文章来源】：江苏大学江苏省

【文章页数】：133 页

【学位级别】：博士

【文章目录】：
DEDICATION
ABSTRACT
摘要
Chapter 1 Introduction
    1.1 Background of least squares
    1.2 Significance of the study
    1.3 Challenges in the Least Squares Problems
    1.4 Contributions of the Dissertation
    1.5 The Organization of Dissertation
Chapter 2 Related Works
    2.1 Introduction
    2.2 Regression
        2.2.1 Single Model Regression(Linear regression)
            2.2.1.1 Ridge Regression
            2.2.1.2 Lasso Regression
            2.2.1.3 ElasticNet Regression
            2.2.1.4 Linear Regression
        2.2.2 Non-linear based regression
            2.2.2.1 Kernel Ridge Regression
            2.2.2.2 Support Vector Regression
        2.2.3 Ensemble Model Regression
            2.2.3.1 Random Forest
            2.2.3.2 Gradient Boosting
            2.2.3.3 Adaboost
            2.2.3.4 Decision Tree Regression
    2.3 Classification
        2.3.1 Decision Tree
        2.3.2 Boosting
Chapter 3 Co-Regularized Kernel Ensemble Regression
    3.1 Introduction
    3.2 RKHS and the Representer Theorem
    3.3 Kernel ridge regression and ensemble model
    3.4 The proposed Method
        3.4.1 Co-regularized kernel ensemble regression
    3.5 Experimental Results
        3.5.1 Dataset description
        3.5.2 Experimental settings
        3.5.3 Performance Evaluations and comparisons
        3.5.4 Classification
            3.5.4.1 Data description
        3.5.5 Digits Recognition
    3.6 Brief Summary
Chapter 4 Coupled Least Squares Support Vector Ensemble Machines
    4.1 Introduction
    4.2 Related works in coupled idea
    4.3 The Proposed Method
        4.3.1 Coupled least squares support vector ensemble machine(C-LSSVEM)
    4.4 Experiments
    4.5 Experimental Settings
    4.6 Experimental results
        4.6.1 Artificial dataset
        4.6.2 UCI datasets
        4.6.3 Handwritten digits-datasets
        4.6.4 NWPU-RESISC45 dataset
    4.7 Brief summary
Chapter 5 Sample-Induced Factorization Kernel Ensemble Regression
    5.1 Introduction
    5.2 Sample-induced factoring idea
    5.3 The Proposed Method
        5.3.1 Sample-induced factorization kernel ensemble regression
    5.4 Experimental results
    5.5 Parameter setting
        5.5.1 Regression on UCI Dataset
        5.5.2 Classification experiments
    5.6 Classification and computer vision dataset
    5.7 Brief summary
Chapter 6 General Conclusions and Future Works
    6.1 General Conclusions
    6.2 Contributions
    6.3 Future Work
References
Acknowledgement
Publications


【参考文献】：
期刊论文
[1]基于电子商务用户行为的同义词识别[J]. 张书娟,董喜双,关毅.  中文信息学报. 2012(03)



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