Abstract
We construct a Teichmuller space for the C1+-conjugacy classes of hyperbolic dynamical systems on surfaces. After introducing the notion of an HR structure which associates an affine structure with each of the stable and unstable laminations, we show that there is a one-to-one correspondence between these HR structures and the C1+-conjugacy classes. As part of the proof we construct a canonical representative dynamical system for each HR structure. This has the smoothest holonomies of any representative of the corresponding C1+-conjugacy class. Finally, we introduce solenoid functions and show that they provide a good Teichmuller space.

Abstract
The series of graphs presented here, based on standard time series diagnostics and display graphs, eases the tasks of identifying and checking an ARIMA model. Each diagnostic display consists of a matrix of plots for a series of ARIMA(p, d, q) models (with p = 1:p(max), q =1:q(max) and d constant). In this way the identification phase of the analysis is eased because the analyst can directly see the incremental effect of each proposed term. The direct visual comparison of the models is helpful to the experienced analyst because it makes immediate the difference in the explanatory capabilities of the various models. The series of plots is very helpful in presenting time series concepts, particularly the identification phase, to introductory classes. The plots have been implemented using the Trellis system in S-Plus.

Abstract
In this article we show how the accuracy of a rule based first order theory may be increased by combining it with a case-based approach in a classification task. Case-based learning is used when the rule language bias is exhausted. This is achieved in an iterative approach. In each iteration theories consisting of first order rules are induced and covered examples are removed. The process stops when it is no longer possible to find rules with satisfactory quality. The remaining examples are then handled as cases. The case-based approach proposed here is also, to a large extent, new, Instead of only storing the cases as provided, it has a learning phase where, for each case, it constructs and stores a set of explanations with support and confidence above given thresholds. These explanations have different levels of generality and the maximally specific one corresponds to the case itself The same case may have different explanations representing different perspectives of the case. Therefore, to classify a new case, it looks for relevant stored explanations applicable to the new case. The different possible views of the case given by the explanations correspond to considering different sets of conditions/features to analyze the case. In other words, they lead to different ways to compute similarity between known cases/explanations and the new case to be classified (as opposed to the commonly used global metric). Experimental results have been obtained on a corpus of Portuguese texts for the task of part-of-speech tagging with significant improvement.

Abstract
RAMSYS is a web-based infrastructure for collaborative data mining. It is being developed in the SolEuNet European Project for virtual enterprise services in data mining and decision support. Central to RAMSYS is the idea of sharing the current best understanding to foster efficient collaboration. This paper presents the design and rationale of Zeno, a core component of RAMSYS. Zeno is a groupware for discourses on the Internet and, for RAMSYS, aims to provide a “virtual data mining laboratory” to aid data miners in collaboratively producing better solutions to data mining problems. © Springer-Verlag Berlin Heidelberg 2001.

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