Multiple Instance Classification via Successive Linear Programming
نویسندگان
چکیده
منابع مشابه
Multiple Instance Classification via Successive Linear Programming
The multiple instance classification problem [6,2,12] is formulated using a linear or nonlinear kernel as the minimization of a linear function in a finite dimensional (noninteger) real space subject to linear and bilinear constraints. A linearization algorithm is proposed that solves a succession of fast linear programs that converges in a few iterations to a local solution. Computational resu...
متن کاملMultiple-Instance Learning via Disjunctive Programming Boosting
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 learning as a special case. Our approach is based on a generalization of linear programming boosting and uses results from disjunctive pro...
متن کاملMax-margin Multiple-Instance Learning via Semidefinite Programming
In this paper, we present a novel semidefinite programming approach for multiple-instance learning. We first formulate the multipleinstance learning as a combinatorial maximummargin optimization problem with additional instance selection constraints within the framework of support vector machines. Although solving this primal problem requires non-convex programming, we nevertheless can then der...
متن کاملSingle- vs. multiple-instance classification
In multiple-instance (MI) classification, each input object or event is represented by a set of instances, named a bag, and it is the bag that carries a label. MI learning is used in different applications where data is formed in terms of such bags and where individual instances in a bag do not have a label. We review MI classification from the point of view of label information carried in the ...
متن کاملMultiple testing via successive subdivision
A sequential multiple testing procedure recently introduced by Heinrich, Bach and Kornmeier allows to “zoom in” on, and thus identify regions with highly significant departures from null-hypotheses. The purpose of this note is to state a cognate of this procedure in general form and to prove that it controls the familywise error. Two possible applications are briefly indicated.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Optimization Theory and Applications
سال: 2007
ISSN: 0022-3239,1573-2878
DOI: 10.1007/s10957-007-9343-5