In order to improve the performance of machine learning in big data, online multiple kernel learning algorithms are proposed in this paper. First, a supervised online multiple kernel learning algorithm for big data (SOMK_bd) is proposed to reduce the computational workload during kernel modification. In SOMK_bd, the traditional kernel learning algorithm is improved and kernel integration is onl...

In a Bayesian approach to online learning a simple approximate parametric form for posterior is updated in each online learning step. Usually in online learning only an estimate of the solution is updated. The Bayesian online approach is applied to two simple learning scenarios, learning a perceptron rule with respectively a spherical and a binary weight prior. In the rst case we rederive the r...

Background. Blended learning (BL) requires a virtual learning and online environment (VLE), which makes available a process for establishing learning communities. The Faculty of Physical Education at the University of Jordan designed several courses which incorporate blended learning with contact classes and online components on e-learning models. Objectives. The present study is to investigat...

Introduction: Online formative assessments (OFA’s) have beenincreasingly recognised in medical education as resources thatpromote self-directed learning. Formative assessments are usedto support the self-directed learning of students. Online formativeassessments have been identified to be less time consuming withautomated feedback. This pilot study aimed to determine whetherparticipation and pe...

On-line learning algorithms are particularly suitable for developing interactive computational agents. These algorithm can be used to teach the agents the abilities needed for engaging in social interactions with humans. If humans are used as teachers in the context of on-line learning algorithms a serious challenge arises: their lack of commitment and availability during the required extensive...

In this work we propose a random field approach to unsupervised machine learning, classifier training and pattern classification. The proposed method treats each sample as a random field and attempts to assign an optimal cluster label to it so as to partition the samples into clusters without a priori knowledge about the number of clusters and the initial centroids. To start with, the algorithm...

This paper aims to study Vygotsky’s (1987) sociocultural theory of learning with respect to how it relates to technology-based second language learning and teaching. The researchers selected their participants from advanced students from Payame Noor University. We divided the participants into two groups- an experimental group and a control group. After teaching the course an experimental group...

In online algorithm selection (OAS), instances of an algorithmic problem class are presented to agent one after another, and the has quickly select a presumably best from fixed set candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers algorithm's runtime. As latter is known exhibit heavy-tail distribution, normally stopped when exceeding predefined u...

In this paper, we present an improved version of the online phase-space learning algorithm of Tsung and Cottrell (1995), called ARTISTE (Autonomous Real-TIme Selection of Training Examples). The new version’s advantages derive from an online adaptive learning rate that depends on the error. We demonstrate the algorithm’s efficacy on two problems: learning a pair of sine waves offset by 901 and ...

As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank approaches, retrieval systems can learn directly from implicit feedback, while they are running. In such an online setting, algorithms need to both explore new solutions to obtain feedback for effective learning, and exploit what has alr...