Go to:
Logótipo
Comuta visibilidade da coluna esquerda
Você está em: Start > Publications > View > Sampling-based relative landmarks: Systematically test-driving algorithms before choosing
Publication

Publications

Sampling-based relative landmarks: Systematically test-driving algorithms before choosing

Title
Sampling-based relative landmarks: Systematically test-driving algorithms before choosing
Type
Article in International Conference Proceedings Book
Year
2001
Authors
Soares, C
(Author)
FEP
View Personal Page You do not have permissions to view the institutional email. Search for Participant Publications View Authenticus page View ORCID page
Petrak, J
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Brazdil, P
(Author)
FEP
View Personal Page You do not have permissions to view the institutional email. Search for Participant Publications View Authenticus page Without ORCID
Conference proceedings International
Pages: 88-95
10th Portuguese Conference on Artificial Intelligence, EPIA 2001
Porto, 17 December 2001 through 20 December 2001
Indexing
Publicação em ISI Web of Knowledge ISI Web of Knowledge
Other information
Authenticus ID: P-008-6VC
Abstract (EN): When facing the need to select the most appropriate algorithm to apply on a new data set, data analysts often follow an approach which can be related to test-driving cars to decide which one to buy: apply the algorithms on a sample of the data to quickly obtain rough estimates of their performance. These estimates are used to select one or a few of those algorithms to be tried out on the full data set. We describe sampling-based landmarks (SL), a systematization of this approach, building on earlier work on landmarking and sampling. SL are estimates of the performance of algorithms on a small sample of the data that are used as predictors of the performance of those algorithms on the full set. We also describe relative landmarks (RL), that address the inability of earlier landmarks to assess relative performance of algorithms. RL aggregate landmarks to obtain predictors of relative performance. Our experiments indicate that the combination of these two improvements, which we call Sampling-based Relative Landmarks, are better for ranking than traditional data characterization measures. © Springer-Verlag Berlin Heidelberg 2001.
Language: English
Type (Professor's evaluation): Scientific
Documents
We could not find any documents associated to the publication.
Recommend this page Top
Copyright 1996-2025 © Faculdade de Direito da Universidade do Porto  I Terms and Conditions  I Acessibility  I Index A-Z
Page created on: 2025-08-06 at 06:10:54 | Privacy Policy | Personal Data Protection Policy | Whistleblowing