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Stuart Andrews

dblp:10/3101 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-authorHuman-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Learning paradigms · 41% Optimization for machine learning · 22% Kernel, tree and ensemble methods · 19%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning paradigms
multiple instance learning
0.122003
Multiple-Instance Learning via Disjunctive Programming Boosting · NIPS 2003
Support Vector Machines for Multiple-Instance Learning · NIPS 2002
Robotics › Motion planning and robot control
mixed integer quadratic programming
0.012002
Support Vector Machines for Multiple-Instance Learning · NIPS 2002
Machine learning › Kernel, tree and ensemble methods
support vector machine
0.012002
Support Vector Machines for Multiple-Instance Learning · NIPS 2002

Methods — techniques the papers use, named apart from their topics

linear relaxation · 0.0column generation · 0.0kernel methods · 0.0heuristic optimization · 0.0
YearPublicationVenuePosition
2004 JointViewer - An Interactive System for Exploring Orthopedic Data
abstract
We present JointViewer, a software tool to aid orthopedics researchers in exploring complex, in-vivo joint kinematics. Given bone-geometry data and bone-motion information, JointViewer models and visualizes bone inter-spacing in the joint. Next, it proposes and displays plausible ligament paths which connect bones together. Both types of models are constructed through a distancefield approach. Users can maneuver the bones in a joint for better viewing, see motion relative to a specific bone, or remove bones from a joint. We demonstrate JointViewer’s effectiveness in three applications: examining normal human wrist kinematics, capturing the effect of injury on forearm kinematics, and exploring the kinematic constraints imposed by ligaments in a pigeon shoulder. In all applications, the system effectively highlights subtle yet important relationships among bones and soft-tissue that in previous standard joint visualizations had gone unnoticed.
G. Elisabeta Marai, Çagatay Demiralp, Stuart Andrews, David H. Laidlaw
IEEE Visualization3
2003 Multiple-Instance Learning via Disjunctive Programming Boosting
abstract
Learning from ambiguous training data is highly relevant in many applications. We present a new learning algorithm for classification problems where labels are associated with sets of pattern instead of individual patterns. This encompasses multiple instance learn- ing as a special case. Our approach is based on a generalization of linear programming boosting and uses results from disjunctive programming to generate successively stronger linear relaxations of a discrete non-convex problem.
Stuart Andrews, Thomas Hofmann 0001
NIPS1
2002 Support Vector Machines for Multiple-Instance Learning
abstract
This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our generalization of SVMs makes a state-of-the-art classification technique, including non-linear classification via kernels, available to an area that up to now has been largely dominated by special purpose methods. We present experimental results on a pharma(cid:173) ceutical data set and on applications in automated image indexing and document categorization.
Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann 0001
NIPS1