Pareto-Path Multi-Task Multiple Kernel Learning

A traditional and intuitively appealing Multi-Task Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing amongst tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a Multi-Objective Optimization (MOO) problem, which considers the concurrent optimization of all task objectives involved in the Multi-Task Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel Support Vector Machine (SVM) MT-MKL framework, that considers an implicitly-defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving better classification performance, when compared to other similar MTL approaches.Comment: Accepted by IEEE Transactions on Neural Networks and Learning System

Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning

We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), using which we establish sharp excess risk bounds for MTL in terms of distribution- and data-dependent versions of the Local Rademacher Complexity (LRC). We also give a new bound on the LRC for norm regularized as well as strongly convex hypothesis classes, which applies not only to MTL but also to the standard i.i.d. setting. Combining both results, one can now easily derive fast-rate bounds on the excess risk for many prominent MTL methods, including---as we demonstrate---Schatten-norm, group-norm, and graph-regularized MTL. The derived bounds reflect a relationship akeen to a conservation law of asymptotic convergence rates. This very relationship allows for trading off slower rates w.r.t. the number of tasks for faster rates with respect to the number of available samples per task, when compared to the rates obtained via a traditional, global Rademacher analysis.Comment: In this version, some arguments and results (of the previous version) have been corrected, or modifie

Reduced-Rank Local Distance Metric Learning

Abstract. We propose a new method for local metric learning based on a conical combination of Mahalanobis metrics and pair-wise similarities between the data. Its formulation allows for controlling the rank of the metrics ’ weight matrices. We also offer a convergent algorithm for training the associated model. Experimental results on a collection of classification problems imply that the new method may offer notable performance advantages over alternative metric learning approaches that have recently appeared in the literature

Business Model Configurations in the Consulting Sector

The current global financial crisis might have been pledging deeply economies and various business sectors around the world, but not that deeply the KIBS, and particularly the consulting industry, in Finland. To comprehend the particular phenomenon, the research lens is steered towards the successful Finnish business consulting companies and their business models. The particular lens investigates maps of different business model configurations which represent combinations of business model components related to the internal and the external environment of a company. Such focus captures a hollistic picture of the Finnish business consulting industry and the way involved companies operate. Hence, the motivation for the execution and the primary objective of this study is to identify the types of business model configurations successful Finnish business consulting companies apply. There are two key theoretical areas that this thesis examines so to provide a solid picture of the different types of business model configuration in the Finnish consulting industry. Firstly, it appears important to comprehend the business model concept and identify the different suggested-in-time configurations through a systematic literature review, and secondly to understand the nature and the behaviour of the KIBS companies, and particularly of the consulting ones. Hence, the literature part of this thesis examines retrospectively and systematically published articles in journals and books regarding these two key theoretical areas. To further extent and in support to the drawing of a holistic picture, the thesis introduces findings of a qualitative empirical study from the Finnish business consulting industry by using semi-structured interviews with people from the higher levels of the companies. The findings of the study suggest that there are 29 different types of business model configurations applied by the business and management consulting firms in Finland. Upon their commonalities, these types were assigned to configurational patterns. In particular, the six types form a pattern of two levels and each level consists of three distinct types of configurations. Two more types of configurations out of the 29 are also deduced each distinctly to a pattern. The rest of the 21 types of business model configurations are claimed as individual types of business model configurations that cannot be patterned further. The name and the description of each pattern and each configuration are all available under the section with the name Synthesis.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Untersuchung des Einflusses der Patientenkörperanatomie auf die Dosimetrie und Entwicklung eines analytischen Dosisberechnungsmodells für die 192-Ir HDR Brachytherapie

Für die Durchführung der konformalen 192Ir HDR Brachytherapie ist eine genaue Dosisberechnung im Patientenkörper unerlässlich. Die Brachytherapieplanungssysteme (BPS) berücksichtigen in ihren Dosisberechnungen gegenwärtig die inhomogene Körperanatomie und die endlichen Körperdimensionen nicht. Diese Arbeit beschäftigt sich mit der Untersuchung des Einflusses der vom als Dosisreferenzmaterial Wasser abweichenden Körperinhomogenitäten und der endlichen Körperdimensionen auf die Dosisberechnung und mit der Entwicklung eines schnellen analytischen Dosisberechnungsmodells für die Korrektur der Dosisverteilung in der Umgebung von 192Ir Strahlern. Hierzu wird anhand der Unterscheidung der Primär- von der Streuphotonendosis und der Sievert Integrationsmethode ein neues Dosisberechnungsmodell entwickelt und in einer Reihe von homogenen, inhomogenen und endlichen Testphantomgeometrien auf seine Rechengenauigkeit verglichen sowie mit Monte Carlo (MC) Simulationsberechnungen überprüft. Die Einsatzgrenzen des Modells werden in zwei klinischen Fallbeispielen durch den Vergleich mit BPS- und MC- Dosisberechnungen jeweils in homogenen und inhomogenen Patientenphantomgeometrien untersucht. Die für die Brachytherapieplanung erwünschte Rechengeschwindigkeit wird von der Fähigkeit des Modells zur Korrektur der Dosisverteilung in inhomogenen patientäquivalenten Phantomgeometrien nicht begrenzt. Zusätzlich wird die BPS-Rechengenauigkeit verglichen, mit den MC-Simulationsberechnungen im dritten klinischen Fallbeispiel mit endlichen Körperdimensionen untersucht und der potentielle Einsatz des vorgestellten Modells diskutiert