Tools and Techniques for Decision Tree Learning

Decision tree learning is an important field of machine learning. In this study we examine both formal and practical aspects of decision tree learning. We aim at answering to two important needs: The need for better motivated decision tree learners and an environment facilitating experimentation with inductive learning algorithms. As results we obtain new practical tools and useful techniques for decision tree learning. First, we derive the practical decision tree learner Rank based on the Findmin protocol of Ehrenfeucht and Haussler. The motivation for the changes introduced to the method comes from empirical experience, but we prove the correctness of the modifications in the probably approximately correct learning framework. The algorithm is enhanced by extending it to operate in the multiclass situations, making it capable of working within the incremental setting, and providing noise tolerance into it. Together these modifications entail practicability through a formal development..

Quality Measures for Improving Technology Trees

The quality of technology trees in digital games can be improved by adjusting their structural and quantitative properties. Therefore, there is a demand for recognizing and measuring such properties. Part of the process can be automated; there are properties measurable by computers, and analyses based on the results (and visualizations of them) may help to produce significantly better technology trees, even practically without extra workload for humans. In this paper, we introduce useful technology tree properties and novel measuring features implemented into our software tool for manipulating technology trees

FEASIBILITY OF B2C CUSTOMER RELATIONSHIP ANALYTICS IN THE B2B INDUSTRIAL CONTEXT

Abstract The purpose of the paper is to evaluate the feasibility of business-to-consumer (B2C) customer relationship analytics in the industrial business-to-business (B2B) context, in particular spare part sales. The contribution of the paper is twofold; the article identifies analytics approaches with value potential for B2B decision-making, and illustrates their value in use. The identified analytics approaches, customer segmentation, market basket analysis and target customer selection, are common in the B2C marketing and e-commerce. However, in the industrial B2B marketing, the application of these approaches is not yet common.. The different kinds of analytics under examination in this paper use machine learning (ML) techniques. The examination takes into account the applicability and usefulness of the techniques as well as implementation challenges. The research suggests that the identified analytics may serve different business purposes and may be relatively straightforward to implement. This requires careful examination of the desired purposes of use in a particular business context. However, the continuous and real-time use of such analyses remains a challenge for further examination also in information systems research. Keywords: Business analytics, B2B decision-making, Machine learning, Data mining, Artificial intelligence, CR

Yksilöllinen opetus ja itsearviointi osana yliopistomatematiikan opintojakson suoritusta

Eriyttämisen tarve ja sähköisten oppimisympäristöjen antama tuki ovat suunnanneet käytännönjärjestelyjä kohti sulautuvaa oppimista ja opetusta. Toisaalta työelämä tarvitsee oppimiskykyisiä ammattilaisia erilaisiin toimiin. Yksi tällainen oppimiskykyä vahvistava työelämätaito on itsearviointi. Teimme Tampereen teknillisellä yliopistolla opetuskokeilun, jossa edellä mainitut asiat yhdistettiin. Opiskelijat tekivät itsenäisesti sähköisestä materiaalista muodostetun oppimiskokonaisuuden, jossa eritasoisilla monipuolisilla sähköisillä tehtävillä ja itsearvioinnilla pyrittiin syvällisempään oppimiseen. Artikkelissa on kuvattu tutkimusta ja siitä saatuja alustavia positiivisia tuloksia. Tutkimukseen osallistui 62 opiskelijaa ja tutkimusaineiston kysymykset olivat osana oppimiskokonaisuuden tehtäviä

Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch

Silver birch (Betula pendula) is a pioneer boreal tree that can be induced to flower within 1 year. Its rapid life cycle, small (440-Mb) genome, and advanced germplasm resources make birch an attractive model for forest biotechnology. We assembled and chromosomally anchored the nuclear genome of an inbred B. pendula individual. Gene duplicates from the paleohexaploid event were enriched for transcriptional regulation, whereas tandem duplicates were overrepresented by environmental responses. Population resequencing of 80 individuals showed effective population size crashes at major points of climatic upheaval. Selective sweeps were enriched among polyploid duplicates encoding key developmental and physiological triggering functions, suggesting that local adaptation has tuned the timing of and cross-talk between fundamental plant processes. Variation around the tightly-linked light response genes PHYC and FRS10 correlated with latitude and longitude and temperature, and with precipitation for PHYC. Similar associations characterized the growth-promoting cytokinin response regulator ARR1, and the wood development genes KAK and MED5A.Peer reviewe

Author Correction: Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch.

In the version of this article initially published, there was a mistake in the calculation of the nucleotide mutation rate per site per generation: 1 × 10−9 mutations per site per generation was used, whereas 9.5 × 10−9 was correct. This error affects the interpretation of population-size changes over time and their possible correspondence with known geological events, as shown in the original Fig. 4 and supporting discussion in the text, as well as details in the Supplementary Note. Neither the data themselves nor any other results are affected. Figure 4 has been revised accordingly. Images of the original and corrected figure panels are shown in the correction notice