18 citations · 52 across the 23 of their papers we have counts for
7 papers · 1 filter
Kernel-based L_2-Boosting with Structure Constraints
Yao Wang, Xin Guo, Shao-Bo Lin
Developing efficient kernel methods for regression is very popular in the past decade. In this paper, utilizing boosting on kernel-based weaker learners, we propose a novel kernel-…
Kernel Interpolation of High Dimensional Scattered Data
Shao-Bo Lin, Xiangyu Chang, Xingping Sun
Data sites selected from modeling high-dimensional problems often appear scattered in non-paternalistic ways. Except for sporadic clustering at some spots, they become relatively f…
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization
Zhi Han, Siquan Yu, Shao-Bo Lin +1
Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques…
Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping
Jinshan Zeng, Min Zhang, Shao-Bo Lin
Boosting is a well-known method for improving the accuracy of weak learners in machine learning. However, its theoretical generalization guarantee is missing in literature. In this…
Distributed Kernel Ridge Regression with Communications
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
This paper focuses on generalization performance analysis for distributed algorithms in the framework of learning theory. Taking distributed kernel ridge regression (DKRR) for exam…
Distributed Learning with Dependent Samples
Zirui Sun, Shao-Bo Lin
This paper focuses on learning rate analysis of distributed kernel ridge regression for strong mixing sequences. Using a recently developed integral operator approach and a classic…
Adaptive Stopping Rule for Kernel-based Gradient Descent Algorithms
Xiangyu Chang, Shao-Bo Lin
In this paper, we propose an adaptive stopping rule for kernel-based gradient descent (KGD) algorithms. We introduce the empirical effective dimension to quantify the increments of…