A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is mi...

Recently, multilayer bootstrap network (MBN) has demonstrated promising performance in unsupervised dimensionality reduction. It can learn compact representations in standard data sets, i.e. MNIST and RCV1. However, as a bootstrap method, the prediction complexity of MBN is high. In this paper, we propose an unsupervised model compression framework for this general problem of unsupervised boots...

the main goal of this research is the explanation and critiques of the concept of learning in paideia proposal that has been done according to adler’s view. in order to achieve this goal, the descriptive-analytic method was used. according to this research, the properties of learning in relation to the content of learning, teaching, the role of teacher, the being active in learning in paideia p...

Active Appearance Models (AAM) are a useful and popular tool for modelling facial variations. They have been used in face tracking, recognition and synthesis applications. For modelling facial dynamics of speech, they have been used in conjunction with Hidden Markov Models (HMM). However, the high dimensionality of the training data and of the resulting AAMs leads to long learning time of HMMs ...

Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine model can be trained from same number of labeled samples. Most existing active for regression (ALR) approaches are supervised, which means sampling process must use some label information, or an model. This paper considers completely unsupervised ALR, i.e., how select without knowing any true...

Introduction: In Pharmacy Diploma Program, mathematics is known as pharmaceutical mathematics. Due to the importance of pharmaceutical mathematics in practice, it is important to have a basic mathematical skill as a basis in calculations in pharmaceutical science. Therefore, it is necessary to create a lecturing condition that enables students more active in understanding the lessons. This rese...

This paper presents an unsupervised segmentation method for feature sequences based on competitivelearning hidden Markov models. Models associated with the nodes of the Self-Organizing Map learn to become selective to the segments of temporal input sequences. Input sequences may have arbitrary lengths. Segment models emerge then on the map through an unsupervised learning process. The method wa...

This paper describes a proof-of-the-principle experiment in which maximum entropy learning is used for the automatic induction of shallow morphological features for the resourcescarce Bantu language of Gı̃kũyũ. This novel approach circumvents the limitations of typical unsupervised morphological induction methods that employ minimum-edit distance metrics to establish morphological similarity bet...