title: Fast bundle algorithm for multiple-instance learning.
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.
authors: Departments of Mathematical Sciences and Electrical, Systems, and Computer Engineering, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180, USA. chbergeron@gmail.com
authors: Charles
authors: C
authors: Bergeron
authors: Gregory
authors: G
authors: Moore
authors: Jed
authors: J
authors: Zaretzki
authors: Curt M
authors: CM
authors: Breneman
authors: Kristin P
authors: KP
authors: Bennett
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D060388:Support Vector Machine