Recent advances of large-scale linear classification
Linear classification is a useful tool in machine learning and data mining. For some data in a
rich dimensional space, the performance (ie, testing accuracy) of linear classifiers has …
rich dimensional space, the performance (ie, testing accuracy) of linear classifiers has …
[PDF][PDF] Change-point detection with feature selection in high-dimensional time-series data
Change-point detection is the problem of finding abrupt changes in time-series, and it is
attracting a lot of attention in the artificial intelligence and data mining communities. In this …
attracting a lot of attention in the artificial intelligence and data mining communities. In this …
A safe double screening strategy for elastic net support vector machine
H Wang, Y Xu - Information Sciences, 2022 - Elsevier
Elastic net support vector machine (ENSVM) is an effective and popular classification
technique. It has been widely used in many practical applications. However, solving large …
technique. It has been widely used in many practical applications. However, solving large …
Fast and robust Block-Sparse Bayesian learning for EEG source imaging
We propose a new Sparse Bayesian Learning (SBL) algorithm that can deliver fast, block-
sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of …
sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of …
A unified view of feature selection based on Hilbert-Schmidt independence criterion
T Wang, Z Hu, H Liu - Chemometrics and Intelligent Laboratory Systems, 2023 - Elsevier
Feature selection is a challenging and increasing important task in the machine learning
and data mining community. According to different learning scenarios such as the …
and data mining community. According to different learning scenarios such as the …