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Creating better models of data work through big exercises of imagination
2020, radio or television broadcast

Found in: ključnih besedah
Summary of found: ...predictive economies, data, social tech, machine learning, autonomy, data worker, trade union, solidarity,...
Keywords: predictive economies, data, social tech, machine learning, autonomy, data worker, trade union, solidarity, labour extraction, data labour rights
Published: 08.12.2020; Views: 89; Downloads: 2
URL Fulltext (0,00 KB)

Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms
Johannes Fuernkranz, Nada Lavrač, Dragan Gamberger, 2010, independent scientific component part or a chapter in a monograph

Abstract: Features are the main rule building blocks for rule learning algorithms. They can be simple tests for attribute values or complex logical terms representing available domain knowledge. In contrast to common practice in classification rule learning, we argue that separation of the feature construction and rule construction processes has theoretical and practical justification. Explicit usage of features enables a unifying framework of both propositional and relational rule learning and we present and analyze procedures for feature construction in both types of domains. It is demonstrated that the presented procedure for constructing a set of simple features has the property that the resulting set enables construction of complete and consistent rules whenever it is possible, and that the set does not include obviously irrelevant features. Additionally, the concept of feature relevancy is important for the effectiveness of rule learning. It this work, we illustrate the concept in the coverage space and prove that the relative relevancy has the quality-preserving property in respect to the resulting rules. Moreover, we show that the transformation from the attribute to the feature space enables a novel, theoretically justified way of handling unknown attribute values. The same approach enables that estimated imprecision of continuous attributes can be taken into account, resulting in construction of robust features in respect to this imprecision.
Found in: ključnih besedah
Summary of found: ... Machine learning, Feature construction, Rule learning, Unknown attribute...
Keywords: Machine learning, Feature construction, Rule learning, Unknown attribute values
Published: 14.07.2017; Views: 1765; Downloads: 0
.pdf Fulltext (365,76 KB)

Mass composition of ultra-high energy cosmic rays at the Pierre Auger Observatory
Gašper Kukec Mezek, 2019, doctoral dissertation

Abstract: Cosmic rays with energies above 10^18 eV, usually referred to as ultra-high energy cosmic rays (UHECR), have been a mystery from the moment they have been discovered. Although we have now more information on their extragalactic origin, their direct sources still remain hidden due to deviations caused by galactic magnetic fields. Another mystery, apart from their production sites, is their nature. Their mass composition, still uncertain at these energies, would give us a better understanding on their production, acceleration, propagation and capacity to produce extensive air showers in the Earth's atmosphere. Mass composition studies of UHECR try to determine their nature from the difference in development of their extensive air showers. In this work, observational parameters from the hybrid detection system of the Pierre Auger Observatory are used in a multivariate analysis to obtain the mass composition of UHECR. The multivariate analysis (MVA) approach combines a number of mass composition sensitive variables and tries to improve the separation between different UHECR particle masses. Simulated distributions of different primary particles are fitted to measured observable distributions in order to determine individual elemental fractions of the composition. When including observables from the surface detector, we find a discrepancy in the estimated mass composition between a mixed simulation sample and the Pierre Auger data. Our analysis results from the Pierre Auger data are to a great degree independent on hadronic interaction models. Although they differ at higher primary masses, the different models are more consistent, when combining fractions of oxygen and iron. Compared to previously published results, the systematic uncertainty from hadronic interaction models is roughly four times smaller. Our analysis reports a predominantly heavy composition of UHECR, with more than a 50% fraction of oxygen and iron at low energies. The composition is then becoming heavier with increasing energy, with a fraction of oxygen and iron above 80% at the highest energies.
Found in: ključnih besedah
Summary of found: ...air showers, mass composition, Pierre Auger Observatory, machine learning, multivariate analysis...
Keywords: astroparticle physics, ultra-high energy cosmic rays, extensive air showers, mass composition, Pierre Auger Observatory, machine learning, multivariate analysis
Published: 03.04.2019; Views: 1228; Downloads: 77
.pdf Fulltext (17,53 MB)

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