把Trans-Lasso算法拓展到了Functional Linear Regression的情况。Structural Interpretability是指考虑functional space 是Reproducing Kernel Hilbert Spaces(RKHS)的情况。使得在其上的量化相似度和distance是可以用RKHS自身的性质解释的。 Residual Importance Weighted Transfer Learning For High-dimensional Linear Regression[J...
Linear regressionTransfer learningDifferential privacyLinear constraintsLassoTransfer learning, as a machine learning approach to enhance model generalization, has found widespread applications across various domains. However, the risk of privacy leakage during the transfer process remains a crucial consideration...
Zhang, Y., & Zhu, Z. (2025). Transfer learning for high-dimensional quantile regression via convolution smoothing. Statistica Sinica, 35, 1–39. High-dimensionalLinear Regression Li, S., Cai, T. T., & Li, H. (2022). Transfer learning for high-dimensional linear regression: Prediction, ...
linear regression. Its theoretical guarantees and minimax optimality will be demonstrated. Next, I will introduce a transferred Q-learning algorithm, which can integrate source data into a target offline or online reinforcement learning problem. Improvement in policy learning will be demonstrated ...
On Transfer Learning in Functional Linear Regression Transfer learning in functional linear regression 迁移学习用于函数式线性回归 1.Introduction and Tutorials (简介与教程) Want to quickly learn transfer learning?想尽快入门迁移学习?看下面的教程。
Transfer learning has ability to create learning task of weakly labeled or unlabeled target domain by using knowledge of source domain to help, which can e
李宏毅机器学习笔记(三)Deep Learning Brief Introduction of Deep Learning(P12) Neural Network 把多个Logistic Regression前后connect在一起,然后把一个Logistic Regression称之为neuron,整个称之为neural network Fully Connect Feedforward Network(全连接前馈网络) 如果一个neural n... ...
In the final phase, a logistic regression (LR) classifier is utilized to determine the performance of various CNN model combinations. A comparative analysis was also performed on classifiers, deep learning, the proposed model, and similar state-of-the-art studies. The experiments demonstrated that ...
Transfer learning for linear regression with differential privacy Transfer learning, as a machine learning approach to enhance model generalization, has found widespread applications across various domains. However, the r... Yiming Hou,Yunquan Song,Zhijian Wang - Complex & Intelligent Systems 被引量:...
Transfer learning has been demonstrated to be effective for many real-world applications as it exploits knowledge present in labeled training data from a source domain to enhance a model’s performance in a target domain, which has little or no labeled target training data. Utilizing a labeled so...