Simulation based on learning methods

The use of simulation technology as a tool for planning and control is of increasing significance in most fields of production. The main part of the expenditure concerning simulation analyses is the modelling of the considered production. Despite the use of modern building-block-oriented modelling technology, this modelling can often not be done by the user, but only by external experts. Against this backdrop, an adaptive simulation system is being developed by the Institute for Industrial Manufacturing and Management (IFF) at the University of Stuttgart. It independently adapts to real production processes, i.e. it learns about the interdependencies of production processes, and, in this way, supports the user in constructing and maintaining the model. In terms of information technology, the research in the field of artificial intelligence, especially in the subdomain of machine learning, is the basis for the realization of such adaptive systems.     

Modeling users through an expert system and a neural network

With the number of Internet and Web users increasing rapidly, electronic service providers are competing to satisfy and better serve customers looking for information or channels of advertisement. A wide variety of browses, specialized sites, custom made software, etc. are being offered on a regular basis. However, the user has to filter through a large number of files before finding what he/she is really looking for. This paper presents a user modeling expert system, SIGMA, based on neural networks for encapsulating Internet and Web users' habits and preferences. SIGMA is an artificial intelligence application designed to answer an Internet client needs and preferences. It analyses the user supplied demographic data and the monitored transactions then generate a tailored profile that is ultimately used to filter what information is being passed on to him/her in an effort to reduce and hopefully eliminate the time and energy expended in sifting through raw and often unwanted data.     

A GENERALIZED GOODNESS CRITERION FOR UNSUPERVISED NEURAL LEARNING OF VISUAL PERCEPTION

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The Relaxed Online Maximum Margin Algorithm

We describe a new incremental algorithm for training linear threshold functions: the Relaxed Online Maximum Margin Algorithm, or ROMMA. ROMMA can be viewed as an approximation to the algorithm that repeatedly chooses the hyperplane that classifies previously seen examples correctly with the maximum margin. It is known that such a maximum-margin hypothesis can be computed by minimizing the length of the weight vector subject to a number of linear constraints. ROMMA works by maintaining a relatively simple relaxation of these constraints that can be efficiently updated. We prove a mistake bound for ROMMA that is the same as that proved for the perceptron algorithm. Our analysis implies that the maximum-margin algorithm also satisfies this mistake bound; this is the first worst-case performance guarantee for this algorithm. We describe some experiments using ROMMA and a variant that updates its hypothesis more aggressively as batch algorithms to recognize handwritten digits. The computational complexity and simplicity of these algorithms is similar to that of perceptron algorithm, but their generalization is much better. We show that a batch algorithm based on aggressive ROMMA converges to the fixed threshold SVM hypothesis.     

浅谈高职高专数学学习差异生的形成原因及其转化

数学知识将是21世纪人才的必备素质之一,其对于当今大学生尤其是高职高专学校的学生 更显重要,因此,重视做好数学学习有困难学生的转化工作。分析其形成原因,研究其对策十分必要。     

A model to support the search for consensus with conflicting rankings: Multitrident

A general problem of collective decision‐making can be formulated in terms of the N rankings of a discrete set of alternatives expressing the preferences of the N members of the group. Helping to reach a consensus is quite important for team building and organizational development and several contributions were developed by different disciplines like organizational sciences, human resources management and operational research. However, very few models were proposed to support the process of reaching consensus and such models assume a concept of distance between rankings implying an assumption of equal distance between adjacent alternatives. Unfortunately, this hypothesis is hardly acceptable in most cases. In this paper, a model is proposed to search for a ranking in terms of the weights space avoiding the previous assumption and providing useful insights to help decision‐makers adopting such ranking as a consensus decision.     

关于我校2004届学生化学学习情况的调查报告

本文对我校纺化系印染、化纤、环保等专业 2 0 0 4届学生化学学习的情况进行了调查和分析 ,提出教学改革的建议和思考。     

Effect of high-level content organizers on hypertext learning

This study investigates the cognitive abilities involved in hypertext learning and design approaches that can help users. We examined the effects of two types of high-level content organizers - a graphic spatial map and an alphabetical list - on readers’ memory for hypertext structure. In the control condition, a simple “home” page with no navigational aid was offered. Subjects were asked to read the hypertext with the purpose of learning the content, but in the post test phase they also had to recall the layout of nodes and links. Memory for links and page places varied as a function of condition. When a spatial map was available participants reconstructed more accurate formal structure then in the two other conditions. Participants’ memory about page places was the least accurate in the list condition. Results also indicate that participants use the content organizer when it is available in order to orientate during learning from hypertext documents.Our results prove that a content organizer showing the formal structure can facilitate the spatial mapping process. However, an organizer exposing a different structure than the real one would generate a conflict.