Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm

In many situations we have some measurement of confidence on positiveness for a binary label. Thepositiveness" is a continuous value whose range is a bounded interval. We propose a novel learning algorithm called \emph{expectation loss SVM} (e-SVM) that is devoted to the problems where only the positiveness" instead of a binary label of each training sample is available. Our e-SVM algorithm can also be readily extended to learn segment classifiers under weak supervision where the exact positiveness value of each training example is unobserved. In experiments, we show that the e-SVM algorithm can effectively address the segment proposal classification task under both strong supervision (e.g. the pixel-level annotations are available) and the weak supervision (e.g.