Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Alexandru Niculescu-Mizil"'
Publikováno v:
ACM SIGMETRICS Performance Evaluation Review. 41:74-77
In this position paper we argue that the availability of "big" monitoring data on Cyber-Physical Systems (CPS) is challenging the traditional CPS modeling approaches by violating their fundamental assumptions. However, big data alsobrings unique oppo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d5ebe62aa147b416f02f7be26769b994
https://doi.org/10.1145/2627534.2627558
https://doi.org/10.1145/2627534.2627558
Publikováno v:
Machine Learning. 80:245-272
We study on-line decision problems where the set of actions that are available to the decision algorithm varies over time. With a few notable exceptions, such problems remained largely unaddressed in the literature, despite their applicability to a l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6310136a2d49ee3bdd53e06579cc1ab9
https://doi.org/10.1007/s10994-010-5178-7
https://doi.org/10.1007/s10994-010-5178-7
Autor:
Tom Fawcett, Alexandru Niculescu-Mizil
Publikováno v:
Machine Learning. 68:97-106
Classifier calibration is the process of converting classifier scores into reliable probability estimates. Recently, a calibration technique based on isotonic regression has gained attention within machine learning as a flexible and effective way to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::70a0bb03c5977e2082e3d7c0ef6e4be9
https://doi.org/10.1007/s10994-007-5011-0
https://doi.org/10.1007/s10994-007-5011-0
Publikováno v:
WACV
This paper addresses the problem of fine-grained recognition in which local, mid-level features are used for classification. We propose to use the Multi-Kernel Learning framework to learn the relative importance of the features and to select optimal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e48721457c119f2b99d16d09fb6eb8bb
https://doi.org/10.1109/wacv.2014.6836094
https://doi.org/10.1109/wacv.2014.6836094
This chapter contains sections titled: 1 Problem Setup and Notations, 2 Structured Penalty on the Inputs, 3 Optimization Algorithms, 4 Comparison of Optimization Procedures, 5 Structured Output Regression for Correlated Phenome Association, 6 Structu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f845fe1d9ffe51771ba81b6902a945d0
https://doi.org/10.7551/mitpress/9333.001.0001
https://doi.org/10.7551/mitpress/9333.001.0001
Publikováno v:
Journal of bioinformatics and computational biology. 9(2)
Many genes and biological processes function in similar ways across different species. Cross-species gene expression analysis, as a powerful tool to characterize the dynamical properties of the cell, has found a number of applications, such as identi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94a26b09887a77617dc0264f89aaa189
https://pubmed.ncbi.nlm.nih.gov/21523930
https://pubmed.ncbi.nlm.nih.gov/21523930
Autor:
Alexandru Niculescu-Mizil
Publikováno v:
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4af43810ecaf692ccae0e68ab991e770
https://doi.org/10.1145/3248783
https://doi.org/10.1145/3248783
Publikováno v:
ICML
Given a large-scale linked document collection, such as a collection of blog posts or a research literature archive, there are two fundamental problems that have generated a lot of interest in the research community. One is to identify a set of high-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9a3f102a469fa6f3b0f9eb24d29aee7
https://doi.org/10.1145/1553374.1553460
https://doi.org/10.1145/1553374.1553460
Publikováno v:
ICDM
We investigate four previously unexplored aspects of ensemble selection, a procedure for building ensembles of classifiers. First we test whether adjusting model predictions to put them on a canonical scale makes the ensembles more effective. Second,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c32992517b56731cf46f2d0bc52ac22
https://doi.org/10.1109/icdm.2006.76
https://doi.org/10.1109/icdm.2006.76
Publikováno v:
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining.
Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits thei
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cf36a191123a4be60775f1364a653937
https://doi.org/10.1145/1150402.1150464
https://doi.org/10.1145/1150402.1150464