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Support Vector Machines (SVMs), prediction on future unseen data can achieve nearly the same performance as in training stage. For these reasons, we propose an incremental algorithm for semi-supervised learning, when given unlabeled data, the algorithm pause the training procedure, predicts a label with current state of model, and then continue to train the model with complete label data. Incremental Max-Margin Learning for Semi-Supervised Multi-Class Problem 39 Label prediction is defined as following optimization problem based on maxentropy principle max yt , log qt − yt , log yt yt ∈Y (20) where qt is the probability measure embedding E assigning to input sample xt , qt = φ(w xt ) (21) the objective function consists of two part,cross entropy yt , log qt and entropy of yt : yt , log yt , representing confidence of yt given qt .
41, 1305– 1330 (1996) Incremental Max-Margin Learning for Semi-Supervised Multi-Class Problem Taocheng Hu and Jinhui Yu Abstract In this paper, we proposed an incremental max-margin model for semisupervised multi-classification learning, where efficient and accuracy need to be considered. Three notable properties are introduced: (1) the model predicts a label for unlabeled sample instance in runtime, and trained with the complete sample instance, while unlabeled and labeled sample instances are unified in our objective function; (2) since the objective function of our model is convex, we can design efficient online algorithm with logarithmic regret, it achieve accurate solution with very little overhead; (3) our model is max-margin machine, which provide our model with considerable generalization capability for future unseen data.
The final HLPi is obtained by Eq. (4). 8. 5. j HLPi = HLPi · a + (HLPi−2 + HLPi−1 )/2 · (1 − a) Algorithm 1 shows the procedure for the estimation of HLP. 2 Allocation of Cache In cloud computing the available cache space is allocated to the cloud applications. The cache space is provided on demand in a fine-grained, multiplexed manner.