Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Manuel J. A. Eugster"'
Publikováno v:
Austrian Journal of Statistics, Vol 41, Iss 1 (2016)
Benchmark experiments are the method of choice to compare learning algorithms empirically. For collections of data sets, the empirical performance distributions of a set of learning algorithms are estimated, compared, and ordered. Usually this is don
Externí odkaz:
https://doaj.org/article/a5c32a4599be4278ae83356947dc57d0
Autor:
Manuel J. A. Eugster, Friedrich Leisch
Publikováno v:
Journal of Statistical Software, Vol 30, Iss 8 (2009)
Archetypal analysis has the aim to represent observations in a multivariate data setas convex combinations of extremal points. This approach was introduced by Cutler andBreiman (1994); they dened the concrete problem, laid out the theoretical foundat
Externí odkaz:
https://doaj.org/article/db165ebdace14610be621df0d149dd4d
Autor:
Shafi Kamalbasha, Manuel J. A. Eugster
Publikováno v:
Data Science – Analytics and Applications ISBN: 9783658321819
Controlled experiments (A/B tests or randomized field experiments) are the de facto standard to make data-driven decisions when implementing changes and observing customer responses. The methodology to analyze such experiments should be easily unders
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a314057a9ff445b0d78401a2256d5b94
https://doi.org/10.1007/978-3-658-32182-6_9
https://doi.org/10.1007/978-3-658-32182-6_9
Autor:
Giulio Jacucci, Manuel J. A. Eugster, Petri Myllymäki, Patrik Floréen, Jaakko Peltonen, Samuel Kaski, Dorota Glowacka, Tuukka Ruotsalo
Publikováno v:
ACM TRANSACTIONS ON INFORMATION SYSTEMS. 36(4):1-46
Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that
Autor:
Sohan Seth, Manuel J. A. Eugster
Publikováno v:
Seth, S & Eugster, M 2015, ' Archetypal Analysis for Nominal Observations ', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 5, pp. 849-861 . https://doi.org/10.1109/TPAMI.2015.2470655
Archetypal analysis is a popular exploratory tool that explains a set of observations as compositions of few ‘pure’ patterns. The standard formulation of archetypal analysis addresses this problem for real valued observations by finding the appro
Autor:
Oswald Barral, Samuel Kaski, Giulio Jacucci, Michiel M. Spapé, Tuukka Ruotsalo, Niklas Ravaja, Manuel J. A. Eugster
Publikováno v:
Scientific Reports
Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user’s interest or search intention is necessary to recommend and retrieve relevant information from these
Autor:
Sohan Seth, Manuel J. A. Eugster
Publikováno v:
Seth, S & Eugster, M J A 2016, ' Probabilistic archetypal analysis ', Machine Learning, vol. 102, no. 1, pp. 85-113 . https://doi.org/10.1007/s10994-015-5498-8
Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes. The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real valued.
Autor:
Ingolf Steffan-Dewenter, Iris Gallenberger, Martin M. Gossner, Carolin Strobl, Jörg Müller, Wolfgang W. Weisser, Beate Wende, Karl Eduard Linsenmair, Manuel J. A. Eugster, Andreas Floren
Publikováno v:
Journal of Applied Ecology. 52:753-762
Summary 1. Among saproxylic beetles, many early colonizers prefer particular host species. Ranking of preferred hosts of local saproxylic beetle communities is critical for effective dead-wood management in forests, but is rarely done because experim
Autor:
Michiel M. Spapé, Niklas Ravaja, Giulio Jacucci, Oswald Barral, Ilkka Kosunen, Manuel J. A. Eugster, Samuel Kaski, Tuukka Ruotsalo
Publikováno v:
BCIforReal@IUI
Automatic annotation of media content has become a critically important task for many digital services as the quantity of available online media content has grown exponentially. One approach is to annotate the content using the physiological response
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f57f69f3e839620151b5b5c65d2e63c5
http://hdl.handle.net/10138/308677
http://hdl.handle.net/10138/308677
Publikováno v:
Computational Statistics & Data Analysis. 71:986-1000
It is common knowledge that certain characteristics of data sets -- such as linear separability or sample size -- determine the performance of learning algorithms. In this paper we propose a formal framework for investigations on this relationship.Th