Types of cost in inductive concept learning

Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In real-world applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost measure). A few papers have investigated the cost of misclassification errors. Very few papers have examined the many other types of cost. In this paper, we attempt to create a taxonomy of the different types of cost that are involved in inductive concept learning. This taxonomy may help to organize the literature on cost-sensitive learning. We hope that it will inspire researchers to investigate all types of cost in inductive concept learning in more depth

Various Types of Learning with Types

International audienceThis paper suggests another look on already known grammatical inference approaches based on specialization strategies

Hands on - hands off: on hitting your thumb with a virtual hammer

In a wired world even the most physically embodied craft skills are affected by computer facilitated communication. To consider how different sorts of space – both real and virtual – influence the learning of craft skills this paper presents three types of space – the ‘real’ space of a jewellery workshop, an online ‘wiki’ space for learning how to make a folding knife mediated by face to face interaction and an online discussion group about French Horn making. Some features common to the learning of any craft skill are discussed as well as some current ideas about the influence of networked communication on the way people relate to each other. Conclusions are drawn about the relationships between different types of learner, different types of skill and different types of learning space which demonstrate that while there may be no substitute for face to face contact in learning the most embodied craft skills, even in real-world settings a significant proportion of learning depends on social interaction which may be reproduced online. Keywords: Craft learning; Apprenticeship; Communities of Practice; Online Networks</p

Do not forget: Full memory in memory-based learning of word pronunciation

Memory-based learning, keeping full memory of learning material, appears a viable approach to learning NLP tasks, and is often superior in generalisation accuracy to eager learning approaches that abstract from learning material. Here we investigate three partial memory-based learning approaches which remove from memory specific task instance types estimated to be exceptional. The three approaches each implement one heuristic function for estimating exceptionality of instance types: (i) typicality, (ii) class prediction strength, and (iii) friendly-neighbourhood size. Experiments are performed with the memory-based learning algorithm IB1-IG trained on English word pronunciation. We find that removing instance types with low prediction strength (ii) is the only tested method which does not seriously harm generalisation accuracy. We conclude that keeping full memory of types rather than tokens, and excluding minority ambiguities appear to be the only performance-preserving optimisations of memory-based learning.Comment: uses conll98, epsf, and ipamacs (WSU IPA

Distilling lessons from across different types of e-learning interventions

Most Higher Education institutions are now embracing e learning to some extent. Some have instigated institution-wide e-learning initiatives, whilst others are engaging in the use of e-learning to expand their portfolio of course provision and the development of new niche markets. But the successful deployment of e-learning, whether large-scale or more localised, is complex and multi-faceted. This paper considers these complexities by drawing on the findings across four disparate e-learning evaluations, arguing that these provide a valuable means of extrapolating key lessons to ensure better use of e-learning and avoidance of large-scale, spectacular and public disasters, such as the UK e-University