In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning (i.e., ordinary supervised learning). Hitherto, neither theoretical nor experimental analysis has been given to explain this phenomenon. In this paper, we theoretically compare PU (and NU) learning against PN learning...

E-learning exhibits a growing presence at Croatian universities. Its implementation in the higher education system is part of the eff ort to improve the process of higher education and to make a signifi cant step forward in turning the Republic of Croatia into information and knowledge society. In these eff orts, one of the biggest problems is the unequal level of success in introducing e-learn...

The prosperous development of wireless communication and sensor technologies has attracted the attention of researchers from both computer and education fields. Various investigations have been made for applying the new technologies to education purposes, such that more active and adaptive learning activities can be conducted in the real world. Nowadays, ubiquitous learning (u-learning) has bec...

M-learning is convenient in that it is accessible from virtually anywhere and collaborative. M-learning also brings strong portability by replacing print material and notes with small computer devices with contents as required. Elearning applications and processes include Web-based learning, computerbased learning, virtual classroom settings and digital collaboration. Content is delivered via e...

background: despite the implementation of new training programs, neglecting individual differences in terms of learning styles can be taken into account as one of the reasons for poor levels of self-care behaviors in patients suffering from diabetes. aim: the purpose of the present study was to examine the effect of training according to the learning styles on self-care behaviors among individu...

In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning (i.e., ordinary supervised learning). Hitherto, neither theoretical nor experimental analysis has been given to explain this phenomenon. In this paper, we theoretically compare PU (and NU) learning against PN learning...

The prosperous development of wireless communication and sensor technologies has attracted the attention of researchers from both computer and education fields. Various investigations have been made for applying the new technologies to education purposes, such that more active and adaptive learning activities can be conducted in the real world. Nowadays, ubiquitous learning (u-learning) has bec...

There are many problems with applying the machine learning technique, which is widely used in the conventional healthcare field, during the mobile u-health service analysis step. First, research on the mobile u-health service is just beginning, and there are very few cases where the existing techniques have been applied in the mobile u-health service environment. Second, since the machine learn...

There are many problems with applying the machine learning technique, which is widely used in the conventional healthcare field, during the mobile u-health service analysis step. First, research on the mobile u-health service is just beginning, and there are very few cases where the existing techniques have been applied in the mobile u-health service environment. Second, since the machine learn...