Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

MONALISA 2.0 and the sea traffic management - a concept creating the need for new maritime information standards and software solutions

Postprint (published version)

Efficient installed base information management and utilization in global after sales service business

This Master´s thesis explores how the a global industrial corporation’s after sales service department should arrange its installed base management practices in order to maintain and utilize the installed base information effectively. Case company has product-related records, such as product’s lifecycle information, service history information and information about product’s performance. Information is collected and organized often case by case, therefore the systematic and effective use of installed base information is difficult also the overview of installed base is missing. The goal of the thesis study was to find out how the case company can improve the installed base maintenance and management practices and improve the installed base information availability and reliability. Installed base information management practices were first examined through the literature. The empirical research was conducted by the interviews and questionnaire survey, targeted to the case company’s service department. The research purpose was to find out the challenges related to case company´s service department’s information management practices. The study also identified the installed base information needs and improvement potential in the availability of information. Based on the empirical research findings, recommendations for improve installed base management practices and information availability were created. Grounding of the recommendations, the case company is suggested the following proposals for action: Service report development, improving the change management process, ensuring the quality of the product documentation in early stages of product life cycle and decision to improve installed base management practices.

Information exchange and disruption risk in multimodal maritime supply chains

Supply chains are becoming increasingly dependent on information ex-change in today’s world, and any disruption can cause severe repercus-sions to the flow of materials in the chain. The speed, accuracy and amount of information are key factors. The aim in this thesis is to address a gap in the research by focusing on information exchange and the risks related to it in a multimodal wood supply chain operating between the Baltic States and Finland. The study involved interviewing people engaged in logistics management in the supply chain in question. The main risk the interviewees identified arose from the sea logistics system, which held a lot of different kinds of information. The threat of breakdown in the Internet connection was also found to hinder the operations significantly. A vulnerability analysis was carried out in order to identify the main actors and channels of infor-mation flow in the supply chain. The analysis revealed that the most important and therefore most vulnerable information-exchange channels were those linking the terminal superintendent, the operative managers and the mill managers. The study gives a holistic picture of the investigated supply chain. Information-exchange-related risks varied greatly. One of the most frequently mentioned was the risk of information inaccuracy, which was usually due to the fact that those in charge of the various functions did not fully understand the consequences for the entire chain.

The case of web based information environment in web based weight management services

Abstrakti

Envia – a repository for environmental information access and discovery

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Information Asymmetry and Accounting Conservatism under IFRS Adoption

LaFond and Watts (2008) provide evidence that information asymmetry might be a determinant of accounting conservatism. One implication of their paper is that regulators trying to reduce information asymmetry by lowering the level of accounting conservatism might be wrong. However, there is a trend in moving away from conservative accounting. The typical example is IFRS adoption. Therefore, this paper studies information asymmetry and accounting conservatism under IFRS adoption. The results show that the level of accounting conservatism decreases after mandatory IFRS adoption, but the adoption of IFRS is likely to weaken the relationship between information asymmetry and accounting conservatism. Moreover, this paper investigates how the change of accounting conservatism under IFRS is related to the change in information environment. The finding shows that accounting conservatism increases information environment, supporting the idea that, by providing comparatively credible information, conservative accounting is beneficial to the information environment.

Prostate Cancer and the Search for Novel Biomarkers

The primary objective of this research project was to identify prostate cancer (PCa) -specific biomarkers from urine. This was done using a multi-faceted approach that targeted (1) the genome (DNA); (2) the transcriptome (mRNA and miRNA); and (3) the proteome. Toward this end, urine samples were collected from ten healthy individuals, eight men with PCa and twelve men with enlarged, non-cancerous prostates or with Benign Prostatic Hyperplasia (BPH). Urine samples were also collected from the same patients (PCa and BPH) as part of a two-year follow-up. Initially urinary nucleic acids and proteins were assessed both qualitatively and quantitatively for characteristics either unique or common among the groups. Subsequently macromolecules were pooled within each group and assessed for either protein composition via LC-MS/MS or microRNA (miRNA) expression by microarray. A number of potential candidates including miRNAs were identified as being deregulated in either pooled PCa or BPH with respect to the healthy control group. Candidate biomarkers were then assessed among individual samples to validate their utility in diagnosing PCa and/or differentiating PCa from BPH. A number of potential targets including deregulation of miRNAs 1825 and 484, and mRNAs for Fibronectin and Tumor Protein 53 Inducible Nuclear Protein 2 (TP53INP2) appeared to be indicative of PCa. Furthermore, deregulation of miR-498 appeared to be indicative of BPH. The sensitivities and specificities associated with using deregulation in many of these targets to subsequently predict PCa or BPH were also determined. This research project has identified a number of potential targets, detectable in urine, which merit further investigation towards the accurate identification of PCa and its discrimination from BPH. The significance of this work is amplified by the non-invasive nature of the sample source from which these candidates were derived, urine. Many cancer biomarker discovery studies have tended to focus primarily on blood (plasma or serum) and/or tissue samples. This is one of the first PCa biomarker studies to focus exclusively on urine as a sample source.

Exploring the Connections between Information Literacy and Writing for International Students

a mixed-method investigation of undergraduate and graduate international students' proficiencies in both information literacy and academic writing to see if a relationship exists between them

Recherche efficace d'information sur le Web

Il s'agit d'un atelier donné dans le cadre des semaines de formation continue aux diététistes par le Département de nutrition de l'Université de Montréal en 2002. Après une brève introduction à Internet, on présente les caractéristiques spécifiques aux répertoires versus celles des moteurs de recherche, puis les principaux sites et moteurs de recherche utiles dans le domaine de la nutrition. La deuxième partie de l'atelier consiste à montrer comment utiliser la banque de données PubMed avec des exemples en nutrition.