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An attempt was made to evaluate the Sequential Error Rate (SER) of an SSVEP classification problem with a Bayesian sequential learning algorithm. Sequential Error Rate refers to the average classification error rate windowed over a short trial period. The algorithm was implemented by the Sequential Monte Carlo method. As opposed to the batch learning algorithm, the sequential learning algorithm does...
A camera-based online signature verification system is proposed in this paper. One Web camera is used for data acquisition, and a sequential Monte Carlo method is used for tracking a pen tip. Several distances are computed from an online signature, and a fusion model trained by using AdaBoost combines the distances and computes a final score.Preliminary experiments were performed by using a private...
A main purpose of humanoid robotic research is to develop a socially interactive robot by providing for a certain degree adaptability and flexibility in order to endow the robot with natural interactions with humans. In this paper, a social learning mechanism is proposed for enabling a humanoid robot to learn social behaviors through imitation. To achieve this goal, a novel imitation algorithm is...
Classification problems in dynamical environments are in many fields,including signal processing and pattern recognition. In this paper, we propose a novel Bayesian approach to classification in a dynamical environment. The proposed approach employs natural sequential prior to improve online learning for an online classifier model. By using the natural sequential prior,the proposed approach describes...
We created a human-robot communication system that can adapt to user preferences that can easily change through communication. Even if any learning algorithms are used, evaluating the human-robot interaction is indispensable and difficult. To solve this problem, we installed a machine learning algorithm called interactive evolutionary computation (IEC) into a communication robot named WAMOEBA-3. IEC...
This paper presents a short-term load forecasting technique for summer using an artificial neural network (ANN). The purpose of this study is to forecast accurately daily peak load for a target period using actual data from the same period of the previous several years as training data. This paper describes two methods. In one method, the actual data of each year for the several years earlier are...
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