In this paper, we present a novel semidefinite programming approach for multiple-instance learning. We first formulate the multiple-instance learning as a combinatorial maximum margin 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 derive an equivalent dual formulation that can be relaxed into a novel convex semidefinite programming (SDP). The relaxed SDP has free parameters where T is the number of instances, and can be solved using a standard interior-point method. Empirical study shows promising performance of the proposed SDP in comparison with the support vector machine appr...
We propose a direct approach to learning sparse Support Vector Machine (SVM) prediction models for M...
Multi-instance multi-label learning (MIML) deals with the problem where each training example is ass...
Multi-instance multi-label learning (MIML) deals with the problem where each training example is ass...
Abstract. In this paper, we present a novel semidefinite programming approach for multiple-instance ...
In multiple instance learning problems, patterns are often given as bags and each bag consists of so...
Multi-instance learning and semi-supervised learning are different branches of machine learning. The...
Most existing Multiple-Instance Learning (MIL) algorithms assume data instances and/or data bags are...
We present new unsupervised and semi-supervised training algorithms for multi-class support vector m...
Most existing Multiple-Instance Learning (MIL) algorithms assume data instances and/or data bags are...
The multiple instance classification problem [6,2,12] is formulated using a linear or nonlinear ker...
Many important machine learning problems are modeled and solved via semidefinite programs; examples ...
AbstractIn this paper, we proposed a new multiple-instance learning (MIL) method based on nonparalle...
Many important machine learning problems are modeled and solved via semidefinite programs; examples ...
Several important machine learning problems can be modeled and solved via semidefinite programs. Oft...
Abstract—Multiple-instance problems arise from the situations where training class labels are attach...
We propose a direct approach to learning sparse Support Vector Machine (SVM) prediction models for M...
Multi-instance multi-label learning (MIML) deals with the problem where each training example is ass...
Multi-instance multi-label learning (MIML) deals with the problem where each training example is ass...
Abstract. In this paper, we present a novel semidefinite programming approach for multiple-instance ...
In multiple instance learning problems, patterns are often given as bags and each bag consists of so...
Multi-instance learning and semi-supervised learning are different branches of machine learning. The...
Most existing Multiple-Instance Learning (MIL) algorithms assume data instances and/or data bags are...
We present new unsupervised and semi-supervised training algorithms for multi-class support vector m...
Most existing Multiple-Instance Learning (MIL) algorithms assume data instances and/or data bags are...
The multiple instance classification problem [6,2,12] is formulated using a linear or nonlinear ker...
Many important machine learning problems are modeled and solved via semidefinite programs; examples ...
AbstractIn this paper, we proposed a new multiple-instance learning (MIL) method based on nonparalle...
Many important machine learning problems are modeled and solved via semidefinite programs; examples ...
Several important machine learning problems can be modeled and solved via semidefinite programs. Oft...
Abstract—Multiple-instance problems arise from the situations where training class labels are attach...
We propose a direct approach to learning sparse Support Vector Machine (SVM) prediction models for M...
Multi-instance multi-label learning (MIML) deals with the problem where each training example is ass...
Multi-instance multi-label learning (MIML) deals with the problem where each training example is ass...