Collaborative Learning by Boosting in Distributed Environments

Abstrac

Distributed Kernel Regression: An Algorithm for Training Collaboratively

This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.Comment: To be presented at the 2006 IEEE Information Theory Workshop, Punta del Este, Uruguay, March 13-17, 200

Distributed Learning in Wireless Sensor Networks

The problem of distributed or decentralized detection and estimation in applications such as wireless sensor networks has often been considered in the framework of parametric models, in which strong assumptions are made about a statistical description of nature. In certain applications, such assumptions are warranted and systems designed from these models show promise. However, in other scenarios, prior knowledge is at best vague and translating such knowledge into a statistical model is undesirable. Applications such as these pave the way for a nonparametric study of distributed detection and estimation. In this paper, we review recent work of the authors in which some elementary models for distributed learning are considered. These models are in the spirit of classical work in nonparametric statistics and are applicable to wireless sensor networks.Comment: Published in the Proceedings of the 42nd Annual Allerton Conference on Communication, Control and Computing, University of Illinois, 200

Consistency in Models for Distributed Learning under Communication Constraints

Motivated by sensor networks and other distributed settings, several models for distributed learning are presented. The models differ from classical works in statistical pattern recognition by allocating observations of an independent and identically distributed (i.i.d.) sampling process amongst members of a network of simple learning agents. The agents are limited in their ability to communicate to a central fusion center and thus, the amount of information available for use in classification or regression is constrained. For several basic communication models in both the binary classification and regression frameworks, we question the existence of agent decision rules and fusion rules that result in a universally consistent ensemble. The answers to this question present new issues to consider with regard to universal consistency. Insofar as these models present a useful picture of distributed scenarios, this paper addresses the issue of whether or not the guarantees provided by Stone's Theorem in centralized environments hold in distributed settings.Comment: To appear in the IEEE Transactions on Information Theor

Helping design educators foster collaborative learning amongst design students

This paper discusses the development of online teaching resources that enable design educators to foster collaborative learning amongst students in the design disciplines. These online teaching resources will be made available through the Design Collaboration website. This website was recently set up by Northumbria University, a UK based institution, to provide an online resource for design educators wishing to develop collaborative pedagogies in design education. It currently contains case studies of collaborative student projects but lacks practical teaching resources. As a result, a research project was set up to compliment the current case studies by creating a suite of design-specific tools and resources that will help foster team management and development. Although various institutions have addressed the subject of group work and collaborative learning, there has been no online resource dedicated to the development of practical teaching tools to help design students work and learn together. This paper focuses on showcasing the range of teaching tools and resources developed through classroom-based trials. These resources have been developed specifically in consultation with Northumbria University's design educators and trialled with undergraduate and postgraduate students from different design disciplines. In addition, issues surrounding the translation of these tools into a practical, easy to use and accessible in an online format is discussed. The Icograda World Design Congress 2009 Education Conference is the ideal international platform to share these tools with the wider design education community. More importantly, we hope to grow the website by encouraging other design educators to submit case studies to the website, using it not only as a means of sharing good practice but also as a tool for reflection. The research value is two-fold (a) translating implicit knowledge of collaborative learning into a practical teaching resource and, (b) helping tutors improve their teaching practice, by linking the teaching resource to real experiences through case studies and interviews

Learning in Social Networks: Rationale and Ideas for Its Implementation in Higher Education

The internet has fast become a prevalent medium for collaboration between people and social networks, in particular, have gained vast popularity and relevance over the past few years. Within this framework, our paper will analyse the role played by social networks in current teaching practices. Specifically, we focus on the principles guiding the design of study activities which use social networks and we relate concrete experiences that show how they contribute to improving teaching and learning within a university environment

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels

Virtual pedagogical model: development scenarios

info:eu-repo/semantics/publishedVersio