The computational grid is a new paradigm in parallel and distributed computing systems for realizing a virtual supercomputer over idle resources available in a wide area network like the Internet. Computational Grids are characterized for exploiting highly heterogeneous resources; so, one of the main concerns in developing computational grids is how to effectively map tasks onto heterogeneous r...

In this thesis, novel cooperative spectrum sensing algorithms for cognitive radios (CRs) are developed, which can blindly learn the channel gains between CRs and licensed primary users (PUs), while jointly detecting active PU transmitters at each time instant. A dictionary learning approach is taken to decompose the received signal energy samples per CR into linear combinations of channel gains...

On-line Backpropagation has become very popular and it has been the subject of in-depth theoretical analyses and massive experimentation. Yet, after almost three decades from its publication, it is still surprisingly the source of tough theoretical questions and of experimental results that are somewhat shrouded in mystery. Although seriously plagued by local minima, the batch-mode version of t...

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...

The recently introduced online confidence-weighted (CW) learning algorithm for binary classification performs well on many binary NLP tasks. However, for multi-class problems CW learning updates and inference cannot be computed analytically or solved as convex optimization problems as they are in the binary case. We derive learning algorithms for the multi-class CW setting and provide extensive...

Online decision tree learning algorithms typically examine all features of a new data point to update model parameters. We propose a novel alternative, Reinforcement Learningbased Decision Trees (RLDT), that uses Reinforcement Learning (RL) to actively examine a minimal number of features of a data point to classify it with high accuracy. Furthermore, RLDT optimizes a long term return, providin...

Nowadays, malicious URLs are the common threat to the businesses, social networks, net-banking etc. Existing approaches have focused on binary detection i.e. either the URL is malicious or benign. Very few literature is found which focused on the detection of malicious URLs and their attack types. Hence, it becomes necessary to know the attack type and adopt an effective countermeasure. This pa...

Online Passive-Aggressive (PA) learning is an effective framework for performing max-margin online learning. But the deterministic formulation and estimated single large-margin model could limit its capability in discovering descriptive structures underlying complex data. This paper presents online Bayesian Passive-Aggressive (BayesPA) learning, which subsumes the online PA and extends naturall...