Abstract
This paper presents new measures, based on the induced decision tree, to characterise datasets for meta-learning in order to select appropriate learning algorithms. The main idea is to capture the characteristics of dataset from the structural shape and size of decision tree induced from the dataset. Totally 15 measures are proposed to describe the structure of a decision tree. Their effectiveness is illustrated through extensive experiments, by comparing to the results obtained by the existing data characteristics techniques, including data characteristics tool (DCT) that is the most wide used technique in meta-learning, and Landmarking that is the most recently developed method.

Abstract
Cross-validation (CV) is the most accurate method available for algorithm recommendation but it is rather slow. We show that information about the past performance of algorithms can be used for the same purpose with small loss in accuracy and significant savings in experimentation time. We use a meta-learning framework that combines a simple IBL algorithm with a ranking method. We show that results improve significantly by using a set of selected measures that represent data characteristics that permit to predict algorithm performance. Our results also indicate that the choice of ranking method as a smaller effect on the quality of recommendations. Finally, we present situations that illustrate the advantage of providing recommendation as a ranking of the candidate algorithms, rather than as the single algorithm which is expected to perform best.

Abstract
Several researchers have studied the application of Machine Learning techniques to the task of user modeling. As most of them pointed out, this task requires learning algorithms that should work on-line, incorporate new information incrementality, and should exhibit the capacity to deal with concept-drift. In this paper we present Adaptive Bayes, an extension to the well-known naive-Bayes, one of the most common used learning algorithms for the task of user modeling. Adaptive Bayes is an incremental learning algorithm that could work on-line. We have evaluated Adaptive Bayes on both frameworks. Using a set of benchmark problems from the UCI repository [2], and using several evaluation statistics, all the adaptive systems show significant advantages in comparison against their non-adaptive versions.

Abstract

Abstract
Very little is known about the promotion objectives of small firms. In particular, do differences in promotional campaigns warrant a different set of promotion objectives, and how effective were these objectives? Are there differences in the use of promotion objectives amongst retailers with different national background, but within the same retail category? To address these questions, we questioned 287 clothing and shoe retailers in New Zealand, 161 in Portugal and 328 in Hungary. Retailers were asked questions about the following: (a) what was their most recent promotion campaign; (b) what were the objectives of their recent promotion campaign; (c) how successful was their recent promotion campaign; (d) how has their particular market share changed in the past 12 months; and (e) what fraction of their stock was markdown and what was the average percentage markdown. We then extended the analysis by contrasting the promotion objectives, market share and stock markdown of successful with unsuccessful campaigns. Again the results indicated some significant variations. These results have ramifications for the small firms' advertising strategies across countries, with an emphasis on the objectives of stock clearance and attracting new customers. A major finding was the cross-cultural differences across the three countries studied, mainly reflecting the different stages of economic development.

Abstract
Hyperbolic invariant sets A of C1+gamma diffeomorphisms where either the stable or unstable leaves are 1-dimensional are considered in this paper, Under the assumption that the A has local product structure, the authors prove that the holonomies between the 1-dimensional leaves are C1+alpha for some 0 < alpha < 1.