Fast bundle algorithm for multiple-instance learning
Related Experiment Video
Updated: May 28, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Charles Bergeron1, Gregory Moore, Jed Zaretzki
1Departments of Mathematical Sciences and Electrical, Systems, and Computer Engineering, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180, USA. chbergeron@gmail.com
Abstract:
We present a bundle algorithm for multiple-instance classification and ranking. These frameworks yield improved models on many problems possessing special structure. Multiple-instance loss functions are typically nonsmooth and nonconvex, and current algorithms convert these to smooth nonconvex optimization problems that are solved iteratively. Inspired by the latest linear-time subgradient-based methods for support vector machines, we optimize the objective directly using a nonconvex bundle method. Computational results show this method is linearly scalable, while not sacrificing generalization accuracy, permitting modeling on new and larger data sets in computational chemistry and other applications. This new implementation facilitates modeling with kernels.
Related Concept Videos
Associative Learning
Classical conditioning, also known...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Introduction to Learning
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Multi-input and Multi-variable systems
In the absence of...
Observational Learning
Multicompartment Models: Overview
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...