most citedLinear and Quadratic Discriminant Analysis: Tutorial

84 citations · 174 across the 13 of their papers we have counts for

collaborators

19 papers

eess.SP20203 cited

Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions

Juan Carrillo, Mark Crowley

During the winter season, real-time monitoring of road surface conditions is critical for the safety of drivers and road maintenance operations. Previous research has evaluated the…

cs.CV20204 cited

Design of Efficient Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data

Juan Carrillo, Mark Crowley, Guangyuan Pan +1

Road maintenance during the Winter season is a safety critical and resource demanding operation. One of its key activities is determining road surface condition (RSC) in order to p…

cs.LG2020

Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees

Juan Carrillo, Daniel Garijo, Mark Crowley +3

Climate science is critical for understanding both the causes and consequences of changes in global temperatures and has become imperative for decisive policy-making. However, clim…

stat.ML202022 cited

Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey

Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1

Multidimensional Scaling (MDS) is one of the first fundamental manifold learning methods. It can be categorized into several methods, i.e., classical MDS, kernel classical MDS, met…

cs.CV2020

Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition

Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Human action recognition is one of the important fields of computer vision and machine learning. Although various methods have been proposed for 3D action recognition, some of whic…

cs.AI2020

Active Measure Reinforcement Learning for Observation Cost Minimization

Colin Bellinger, Rory Coles, Mark Crowley +1

Standard reinforcement learning (RL) algorithms assume that the observation of the next state comes instantaneously and at no cost. In a wide variety of sequential decision making…