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In support vector machines (SVM), the kernel functions which compute dot product in feature space significantly affect the performance of classifiers. Each kernel function is suitable for some tasks. A universal kernel is not possible, and the kernel must be chosen for the tasks under consideration by hand. In order to obtain a flexible kernel function, a(More)
The Multiple-Instance Learning (MIL) is a new learning framework proposed in 1997 and getting more attention recently in the field of machine learning. The MIL framework performs successfully on learning from ambiguous examples, whereas standard supervised learning seems to be inefficient. This paper proposes a method called the Multiple-Instance Neural(More)