Learning and Revising User Profiles: The Identification of Interesting Web Sites

We discuss algorithms for learning and revising user profiles that can determine which World Wide Web sites on a given topic would be interesting to a user. We describe the use of a naive Bayesian classifier for this task, and demonstrate that it can incrementally learn profiles from user feedback on the interestingness of Web sites. Furthermore, the Bayesian classifier may easily be extended to revise user provided profiles. In an experimental evaluation we compare the Bayesian classifier to computationally more intensive alternatives, and show that it performs at least as well as these approaches throughout a range of different domains. In addition, we empirically analyze the effects of providing the classifier with background knowledge in form of user defined profiles and examine the use of lexical knowledge for feature selection. We find that both approaches can substantially increase the prediction accuracy.     

Scaffolded writing and rewriting in the discipline: A web-based reciprocal peer review system

This paper describes how SWoRD (scaffolded writing and rewriting in the discipline), a web-based reciprocal peer review system, supports writing practice, particularly for large content courses in which writing is considered critical but not feasibly included. To help students gain content knowledge as well as writing and reviewing skills, SWoRD supports the whole cycle of writing, reviews, back-reviews, and rewriting by scaffolding the journal publication process as its authentic practice model. In addition, SWoRD includes algorithms that compute individual reviewer’s review accuracy, which is in turn used to support the various drawbacks of reciprocal peer reviews (e.g., variation in motivation or ability of reviewers). Finally, this paper describes an empirical evaluation showing that the SWoRD approach is effective in improving writing quality in content classes.     

Improved Estimates for the Accuracy of Small Disjuncts

Learning systems often describe a target class as a disjunction of conjunctions of conditions. Recent work has noted that small disjuncts, i.e., those supported by few training examples, typically have poor predictive accuracy. One model of this accuracy is provided by the Bayes-Laplace formula based on the number of training examples covered by the disjunct and the number of them belonging to the target class. However, experiments show that small disjuncts associated with target classes of different relative frequencies tend to have different error rates. This note defines the context of a disjunct as the set of training examples that fail to satisfy at most one of its conditions. An empirical adaptation of the Bayes-Laplace formula is presented that also makes use of the relative frequency of the target class in this context. Trials are reported comparing the performance of the original formula and the adaptation in six learning tasks.     

Role of the Basal Ganglia in Category Learning: How Do Patients With Parkinson's Disease Learn?

The purpose of the present study was to gain a deeper understanding of the role of the basal ganglia in learning and memory by examining learning strategies among patients with basal ganglia dysfunction. Using a probabilistic category learning task (the "weather prediction" task) previously shown to be sensitive to basal ganglia function, the authors examined patterns of performance during learning and used mathematical models to capture different learning strategies. Results showed that patients with Parkinson's disease exhibit different patterns of strategy use. Specifically, most controls initially used a simple, but suboptimal, strategy that focused on single-cue-outcome associations; eventually, however, most controls adopted a more complex, optimal learning strategy, integrating single-cue associations to predict outcomes for multiple-cue stimuli. In contrast, the majority of individuals with Parkinson's disease continued to rely on simple single-cue learning strategies throughout the experiment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Anthony D. Wagner: Award for Distinguished Scientific Early Career Contributions to Psychology.

Presents the citation for Anthony D. Wagner, who received the Award for Distinguished Scientific Early Career Contributions to Psychology (cognition and human learning) "for outstanding and innovative research on the neural basis of memory and executive control." A brief profile and a selected bibliography accompany the citation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

关于社区教育的若干思考

20世纪80年代，中国开始了社区教育的实验。本论述了缸区教育的概念和发展历史。指出了社区教育的功能以及社区教育在学习化社会中的作用，提出了社区教育发展的思路。     

A distant-learning training module on the environmental design of urban buildings

The present paper aims to present a distant-learning training module that concerns the environmental design of urban buildings. The whole approach attempts to integrate topics that concern the design of urban buildings from various points of views, e.g. active and passive systems, automation systems, indoor air quality, economic aspects, energy and resources management. The package offers both printed and electronic material that gives the possibility to the students to study the various topics by using different educational methods. Additionally, the present package contains software tools that permit the students to examine real or hypothetical situations and to study further the influence of various parameters that concern the environmental building design.     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.     

The impact of the Hebbian learning rule on research in behavioural neuroscience.

Hebb's principal theoretical propositions, the cell assembly and the nature of synaptic change, were generated at a time when the focus of work in behavioural neuroscience was directed at understanding issues such as the principles governing the behaviour of animals in neuropsychological studies of learning and memory and the role of drives in the control of behaviours like sex and feeding and drinking. It was not until attention shifted to understanding the neural underpinnings of learning and memory that Hebb's propositions had an impact on behavioural neuroscience as they provided a simple, and testable, mechanism for synaptic plasticity observed both in learning and in other forms of experience-dependent neural change. But much of the field remains interested in other issues such as sensation and perception, motivation, attention, and so on, and to date, Hebb's propositions have had little impact. (PsycINFO Database Record (c) 2010 APA, all rights reserved)