The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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 recent years, researches on smart environments (SEs) especially smart homes for senior care have attracted great attention. One important issue in constructing such kind of SEs is privacy, because the target environment (e.g. a home or a room) is not public space, and devices like video cameras cannot be used. For individual or personal spaces, the SE should not be too smart to invade the resident's...
Smart environment is a situation aware system for improving the quality of life. This kind of systems are extremely useful for solving or avoiding several issues. The issue we consider in this research is the care of elderly peoples living in their own homes. Usually, information needed for situation awareness is obtained from various devices such as video cameras and voice recorders. However, these...
Neural network tree (NNTree) is a decision tree (DT) in which each internal node contains a neural network (NN). Experimental results show that the performance of the NNTrees is usually better than that of the traditional univariate DTs. In addition, the NNTrees are more usable than the single model fully connected NNs because their structures can be determined automatically in the induction process...
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...
In today's society, information hiding is becoming a key mechanism to protecting one's secrets. In our study, we have proposed several new information hiding methods based on image morphing. To synthesize a large number of virtual but natural images through morphing to cover different kinds of secrets, it is necessary to propose some efficient and effective method for automatic feature point detection...
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...
The goal of this research is to design a multimedia analyzer (MA) that can be embedded in portable devices. This MA can recognize different multimedia (e.g. text and image) patterns and help the user to analyze the multimedia contents more efficiently. To realize the MA in an environment with limited computing resource, we propose a new concept called decision boundary learning (DBL). The basic idea...
In neural network (NN) learning, we usually find an NN to minimize the approximation error for a given training set. Depends on the data given, the performance of the NN can vary significantly. In fact, if the training data are close to the true decision boundary (DB), the NN can generalize well. On the other hand, if the given data are far away from the true DB, the DB formed by the NN can be very...
Neural network tree (NNTree) is a hybrid model for machine learning. So far, we have proposed an efficient algorithm for inducing NNTrees, and verified through experiments that NNTrees are efficient and effective for solving different pattern recognition problems. However, for problems like text categorization, induction of NNTrees can be very computationally expensive. To solve this problem, we have...
Neural network tree (NNTree) is a hybrid model for machine learning. Compared with single model fully connected neural networks, NNTrees are more suitable for structural learning, and faster for decision making. To increase the realizability of the NNTrees, we have tried to induce more compact NNTrees through dimensionality reduction. So far, we have used principal component analysis (PCA) and linear...
In this paper, we propose a new method for face detection by combining a modified linear discriminant analysis (M-LDA) and neural network (NN). Here, M-LDA is used for feature extraction, and NN is used to make the final decision. The M-LDA minimizes the variance within all "face " clusters, and at the same time, maximizes the variance between all "face" clusters and all "non-face"...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.