2,212 research outputs found
Visual Integration of Data and Model Space in Ensemble Learning
Ensembles of classifier models typically deliver superior performance and can
outperform single classifier models given a dataset and classification task at
hand. However, the gain in performance comes together with the lack in
comprehensibility, posing a challenge to understand how each model affects the
classification outputs and where the errors come from. We propose a tight
visual integration of the data and the model space for exploring and combining
classifier models. We introduce a workflow that builds upon the visual
integration and enables the effective exploration of classification outputs and
models. We then present a use case in which we start with an ensemble
automatically selected by a standard ensemble selection algorithm, and show how
we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture
Proceedings of the 15th Conference on Knowledge Organization WissOrg'17 of theGerman Chapter of the International Society for Knowledge Organization (ISKO),30th November - 1st December 2017, Freie Universität Berlin
Wissensorganisation is the name of a series of biennial conferences /
workshops with a long tradition, organized by the German chapter of the
International Society of Knowledge Organization (ISKO). The 15th conference in
this series, held at Freie Universität Berlin, focused on knowledge
organization for the digital humanities. Structuring, and interacting with,
large data collections has become a major issue in the digital humanities. In
these proceedings, various aspects of knowledge organization in the digital
humanities are discussed, and the authors of the papers show how projects in
the digital humanities deal with knowledge organization.Wissensorganisation ist der Name einer Konferenzreihe mit einer langjährigen
Tradition, die von der Deutschen Sektion der International Society of
Knowledge Organization (ISKO) organisiert wird. Die 15. Konferenz dieser
Reihe, die an der Freien Universität Berlin stattfand, hatte ihren Schwerpunkt
im Bereich Wissensorganisation und Digital Humanities. Die Strukturierung von
und die Interaktion mit großen Datenmengen ist ein zentrales Thema in den
Digital Humanities. In diesem Konferenzband werden verschiedene Aspekte der
Wissensorganisation in den Digital Humanities diskutiert, und die Autoren der
einzelnen Beiträge zeigen, wie die Digital Humanities mit Wissensorganisation
umgehen
Separating Agent-Functioning and Inter-Agent Coordination by Activated Modules: The DECOMAS Architecture
The embedding of self-organizing inter-agent processes in distributed
software applications enables the decentralized coordination system elements,
solely based on concerted, localized interactions. The separation and
encapsulation of the activities that are conceptually related to the
coordination, is a crucial concern for systematic development practices in
order to prepare the reuse and systematic integration of coordination processes
in software systems. Here, we discuss a programming model that is based on the
externalization of processes prescriptions and their embedding in Multi-Agent
Systems (MAS). One fundamental design concern for a corresponding execution
middleware is the minimal-invasive augmentation of the activities that affect
coordination. This design challenge is approached by the activation of agent
modules. Modules are converted to software elements that reason about and
modify their host agent. We discuss and formalize this extension within the
context of a generic coordination architecture and exemplify the proposed
programming model with the decentralized management of (web) service
infrastructures
Of course we share! Testing Assumptions about Social Tagging Systems
Social tagging systems have established themselves as an important part in
today's web and have attracted the interest from our research community in a
variety of investigations. The overall vision of our community is that simply
through interactions with the system, i.e., through tagging and sharing of
resources, users would contribute to building useful semantic structures as
well as resource indexes using uncontrolled vocabulary not only due to the
easy-to-use mechanics. Henceforth, a variety of assumptions about social
tagging systems have emerged, yet testing them has been difficult due to the
absence of suitable data. In this work we thoroughly investigate three
available assumptions - e.g., is a tagging system really social? - by examining
live log data gathered from the real-world public social tagging system
BibSonomy. Our empirical results indicate that while some of these assumptions
hold to a certain extent, other assumptions need to be reflected and viewed in
a very critical light. Our observations have implications for the design of
future search and other algorithms to better reflect the actual user behavior
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