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To retrain an existing multilayer perceptron (MLP) on-line using newly observed data, it is necessary to incorporate the new information while preserving the performance of the network. This is known as the “plasticitystability” problem. For this purpose, we proposed an algorithm for on-line training with guide data (OLTA-GD). OLTA-GD is good for implementation in portable/wearable computing devices...
In this paper, we propose a method for generating guide data, and investigate its efficiency and efficacy for on-line learning with guide data. On-line learning in this research updates a learning model initialized by the decision boundary making algorithm proposed by us in our earlier study. The problem is that, if the guide data are not properly generated, on-line learning may require high computational...
We compare the performance of multilayer perceptrons (MLPs) obtained using back propagation (BP), decision boundary making (DBM) algorithm and extreme learning machine (ELM), and investigate better method for developing aware agents (A-agent) that are suitable for implementation in portable/wearable computing devices (P/WCD). The DBM has been proposed by us for inducing compact and high performance...
In this paper, we investigate proper strategies for determining the step size of the backpropagation (BP) algorithm for on-line learning. It is known that for off-line learning, the step size can be determined adaptively during learning. For on-line learning, since the same data may never appear again, we cannot use the same strategy proposed for off-line learning. If we do not update the neural network...
Outlier detection is a method to improve performances of machine learning models. In this paper, we use an outlier detection method to improve the performance of our proposed algorithm called decision boundary making (DBM). The primary objective of DBM algorithm is to induce compact and high performance machine learning models. To obtain this model, the DBM reconstructs the performance of support...
The purpose of our study is to induce compact and high performance machine learning models. In our earlier study, we proposed a decision boundary making (DBM) algorithm. The main philosophy of the DBM algorithm is to reconstruct a high performance model with much smaller cost. In our study, we use support vector machine as a high performance model, and a multilayer neural network, i.e., multilayer...
In recent years, portable computing devices (PCDs) such as smart phones are becoming more and more popular, and many users are using applications on their PCDs. To customize applications for each user, we suggest to use awareness agents (A-agents) that can help users. However, A-agents usually become large. To reduce the size of A-agents, we have proposed decision boundary learning (DBL) based on...
Smartphone, in recent year, becomes popular and has been widely applied by users. In order to meet different needs from users, embedding "awareness" providing supports by understanding onto smartphone devices is necessry. Due to the limitations (e.g. computing resources, etc.) on smartphone, methods that is light but with high performance are strongly expected. In this study, the concept...
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