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 …

Several real problems involve the classification of data into categories or classes. Given a
data set containing data whose classes are known, Machine Learning algorithms can be …

Background The large volume and suboptimal image quality of portable chest X-rays
(CXRs) as a result of the COVID-19 pandemic could post significant challenges for …

The perceived success of recent visual recognition approaches has largely been derived
from their performance on classification tasks, where all possible classes are known at …

Consider the problem of estimating the entries of a large matrix, when the observed entries
are noisy versions of a small random fraction of the original entries. This problem has …

Real-world tasks in computer vision often touch upon open set recognition: multi-class
recognition with incomplete knowledge of the world and many unknown inputs. Recent work …

LIBLINEAR is an open source library for large-scale linear classification. It supports logistic
regression and linear support vector machines. We provide easy-to-use command-line tools …

Predicting driver behavior is a key component for Advanced Driver Assistance Systems
(ADAS). In this paper, a novel approach based on Support Vector Machine and Bayesian …

The rich data provided by high-resolution satellite imagery allow us to directly model aerial
scenes by understanding their spatial and structural patterns. While pixel-and object-based …

Inferring rankings over elements of a set of objects, such as documents or images, is a key
learning problem for such important applications as Web search and recommender systems …