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This paper proposes a complete framework of posterior probability support vector machines (PPSVMs) for weighted training samples using modified concepts of risks, linear separability, margin, and optimal hyperplane. Within this framework, a new optimization problem for unbalanced classification problems is formulated and a new concept of support vectors(More)
BACKGROUND There is much uncertainty about the effects of early lowering of elevated blood pressure (BP) after acute intracerebral haemorrhage (ICH). Our aim was to assess the safety and efficiency of this treatment, as a run-in phase to a larger trial. METHODS Patients who had acute spontaneous ICH diagnosed by CT within 6 h of onset, elevated systolic(More)
Recently increasing attention has been focused on directly optimizing ranking measures and inducing sparsity in learning models. However, few attempts have been made to relate them together in approaching the problem of learning to rank. In this paper, we consider the sparse algorithms to directly optimize the Normalized Discounted Cumulative Gain (NDCG)(More)
A novel neural network is proposed in this paper for realizing associative memory. The main advantage of the neural network is that each prototype pattern is stored if and only if as an asymptotically stable equilibrium point. Furthermore, the basin of attraction of each desired memory pattern is distributed reasonably (in the Hamming distance sense), and(More)
BACKGROUND AND PURPOSE The Intensive Blood Pressure Reduction In Acute Cerebral Haemorrhage Trial (INTERACT) study suggests that early intensive blood pressure (BP) lowering can attenuate hematoma growth at 24 hours after intracerebral hemorrhage. The present analyses aimed to determine the effects of treatment on hematoma and perihematomal edema over 72(More)