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Epilepsy is one of the most common neurological disorders, characterized by recurrent seizures. Being able to predict impending seizures could greatly improve the lives of patients with epilepsy. In this study, we propose a new adaptive learning approach for online seizure prediction based on analysis of electroencephalogram (EEG) recordings. For each individual patient, we construct baseline patterns of normal and preseizure EEG samples, continuously monitor sliding windows of EEG recordings, and classify each window to normal or preseizure using a $(K)$-nearest-neighbor (KNN) method. A new reinforcement learning algorithm is proposed to continuously update both normal and preseizure baseline patterns based on the feedback from prediction result of each window. The proposed approach was evaluated on EEG data from 10 patients with epilepsy. For each one of the 10 patients, the adaptive approach was trained using the recordings containing the first half of seizure occurrences, and tested prospectively on the subsequent recordings. Using a 150-minute prediction horizon, our approach achieved 73 percent sensitivity and 67 percent specificity on average over 10 patients. This result is shown to be far better than those of a nonupdate prediction scheme and two native prediction schemes.