The goal of learning theory is to approximate a function from sample values. To attain this
goal learning theory draws on a variety of diverse subjects, specifically statistics …

We study distributed learning with the least squares regularization scheme in a reproducing
kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a …

How can we select the best performing data-driven model? How can we rigorously estimate
its generalization error? Statistical Learning Theory (SLT) answers these questions by …

An extreme learning machine (ELM) can be regarded as a two-stage feed-forward neural
network (FNN) learning system that randomly assigns the connections with and within …

How can we select the best performing data‐driven model? How can we rigorously estimate
its generalization error? Statistical learning theory (SLT) answers these questions by …

We consider the regression problem by learning with a regularization scheme in a data
dependent hypothesis space and ℓ1-regularizer. The data dependence nature of the kernel …

We study the generalization ability of distributed learning equipped with a divide-and-
conquer approach and gradient descent algorithm in a reproducing kernel Hilbert space …

In this paper, we provide a mathematical foundation for the least square regression learning
with indefinite kernel and coefficient regularization. Except for continuity and boundedness …

Sparse additive models have been successfully applied to high-dimensional data analysis
due to the flexibility and interpretability of their representation. However, the existing …

Spectral algorithms form a general framework that unifies many regularization schemes in
learning theory. In this paper, we propose and analyze a class of thresholded spectral …