Abstract
This paper is concerned with the problem of characterization of classification algorithms. The aim is to determine under what circumstances a particular classification algorithm is applicable. The method used involves generation of different kinds of models. These include regression and rule models, piecewise linear models (model trees) and instance based models. These are generated automatically on the basis of dataset characteristics and given test results. The lack of data is compensated for by various types of preprocessing. The models obtained are characterized by quantifying their predictive capability and the best models are identified. © Springer-Verlag Berlin Heidelberg 1995.

Abstract

Abstract
Sullivan's scaling function provides a complete description of the smooth conjugacy classes of cookie-cutters. However, for smooth conjugacy classes of Markov maps on a train track, such as expanding circle maps and train track mappings induced by pseudo-Anosov systems, the generalisation of the scaling function suffers from a deficiency. It is difficult to characterise the structure of the set of those scaling functions which correspond to smooth mappings. We introduce a new invariant for Markov maps called the solenoid function. We prove that for any prescribed topological structure, there is a one-to-one correspondence between smooth conjugacy classes of smooth Markov maps and pseudo-Hölder solenoid functions. This gives a characterisation of the moduli space for smooth conjugacy classes of smooth Markov maps. For smooth expanding maps of the circle with degree d this moduli space is the space of Hölder continuous functions on the space {0,…, d - 1} satisfying the matching condition.

Abstract
We classify the C1+a structures on embedded trees. This extends the results of Sullivan on embeddings of the binary tree to trees with arbitrary topology and to embeddings without bounded geometry and with contact points. We used these results in an earlier paper to describe the moduli spaces of smooth conjugacy classes of expanding maps and Markov maps on train tracks. In later papers we will use those results to do the same for pseudo-Anosov diffeomorphisms of surfaces. These results are also used in the classification of renormalisation limits of C1+a diffeomorphisms of the circle.

Abstract

Abstract