Abstract
AbstractWe present a fast heuristic approach for solving a binary multiple instance learning (MIL) problem, which consists in discriminating between two kinds of item sets: the sets are called bags and the items inside them are called instances. Assuming that only two classes of instances are allowed, a common standard hypothesis states that a bag is positive if it contains at least a positive instance and it is negative when all its instances are negative. Our approach constructs a MIL separating hyperplane by preliminary fixing the normal and reducing the learning phase to a univariate nonsmooth optimization problem, which can be quickly solved by simply exploring the kink points. Numerical results are presented on a set of test problems drawn from the literature.
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Reference32 articles.
1. Amores J (2013) Multiple instance classification: review, taxonomy and comparative study. Artif Intell 201:81–105
2. Andrews S, Tsochantaridis I, Hofmann T (2003) Support vector machines for multiple-instance learning. In: Becker S, Thrun S, Obermayer K (eds) Advances in neural information processing systems. MIT Press, Cambridge, pp 561–568
3. Astorino A, Frangioni A, Gaudioso M, Gorgone E (2011) Piecewise quadratic approximations in convex numerical optimization. SIAM J Optim 21(4):1418–1438
4. Astorino A, Fuduli A, Gaudioso M (2019a) A Lagrangian relaxation approach for binary multiple instance classification. IEEE Trans Neural Netw Learn Syst 30(9):2662–2671
5. Astorino A, Fuduli A, Gaudioso M, Vocaturo E (2019b) Multiple instance learning algorithm for medical image classification. In: CEUR workshop proceedings, vol. 2400
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