Support Vector Machines for Multiple-Instance Learning
نویسندگان
چکیده
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 pharmaceutical data set and on applications in automated image indexing and document categorization.
منابع مشابه
A Comparative Study of Extreme Learning Machines and Support Vector Machines in Prediction of Sediment Transport in Open Channels
The limiting velocity in open channels to prevent long-term sedimentation is predicted in this paper using a powerful soft computing technique known as Extreme Learning Machines (ELM). The ELM is a single Layer Feed-forward Neural Network (SLFNN) with a high level of training speed. The dimensionless parameter of limiting velocity which is known as the densimetric Froude number (Fr) is predicte...
متن کاملMultiple Instance Twin Support Vector Machines ∗
Considering the multiple instance learning(MIL) in classification problem, a novel multiple instance twin support vector machines(MI-TWSVM) method is proposed. For linear classification, unlike other maximum margin SVM-based MIL methods, the proposed approach leads to two non-parallel hyperplanes. The non-linear classification via kernels is also studied. Preliminary experimental results on pub...
متن کاملNSF-ITR/IM PROJECT: 2002 Abstracts From Bits to Information: Statistical Learning Technologies for Digital Information Management Search
Project Title: Support Vector Machines for Multiple Instance Learning PI: T. Hofmann Participants: Stuart Andrews and Thomas Hofmann Abstract: Multiple Instance Learning (MIL) is an important generalization of standard supervised binary classification. In MIL labels are not available for individual training patterns, but are associated with sets of patterns, which introduces additional uncertai...
متن کاملMining Biological Repetitive Sequences Using Support Vector Machines and Fuzzy SVM
Structural repetitive subsequences are most important portion of biological sequences, which play crucial roles on corresponding sequence’s fold and functionality. Biggest class of the repetitive subsequences is “Transposable Elements” which has its own sub-classes upon contexts’ structures. Many researches have been performed to criticality determine the structure and function of repetitiv...
متن کاملInfluence of Positive Instances on Multiple Instance Support Vector Machines
This work studies the influence of the percentage of positive instances on positive bags on the performance of multiple instance learning algorithms using support vector machines. There are several studies that compare the performance of different types of multiple instance learning algorithms in different datasets and the performance of these algorithms with the supervised learning counterpart...
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 کامل