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The paradigm of statistical inference and the paradigm of statistical learning

100%

Pociecha J.

Przegląd Statystyczny

2021

tom 68

nr 1

1-16

The starting point for the presentation of the similarities and differences between the principles of conducting statistical research according to the rules of both statistical inference and statistical learning is the paradigm theory, formulated by Thomas Kuhn. In the first section of this paper, the essential features of the statistical inference paradigm are characterised, with particular attention devoted to its limitations in contemporary statistical research. Subsequently, the article presents the challenges faced by this research jointly with the expanding opportunities for their effective reduction. The essence of learning from data is discussed and the principles of statistical learning are defined. Moreover, significant features of the statistical learning paradigm are formulated in the context of the differences between the statistical inference paradigm and the statistical learning paradigm. It is emphasised that the statistical learning paradigm, as the more universal one of the two discussed, broadens the possibilities of conducting statistical research, especially in socio-economic sciences.

Philosophical foundations of statistical research

88%

Pociecha J.

Przegląd Statystyczny

2020

tom 67

nr 3

195-211

Every researcher desires to uncover the truth about the object of the undertaken study. When conducting statistical research, however, scientists frequently give no deeper thought as to their motivation underlying the choice of the particular purpose and scope of the study, or the choice of analytical tools. The aim of this paper is to provide a reflection on the philosophical foundations of statistical research. The three basic understandings of the term ‘statistics’ are outlined, followed by a synthetic overview of the understanding of the concept of truth in the key branches of philosophy, with particular attention devoted to the understanding of truth in probabilistic terms. Subsequently, a short discussion is presented on the philosophical bases of statistics, touching upon such topics as determinism and indeterminism, chance and chaos, deductive and inductive reasoning, randomness and uncertainty, and the impact of the information revolution on the development of statistical methods, especially in the context of socio-economic research. The article concludes with the formulation of key questions regarding the future development of statistics.

Predicting length of fatigue cracks by means of machine learning algorithms in the small-data regime

75%

Badora M. , Sepe M. , Bielecki M. , Graziano A. , Szolc T.

Eksploatacja i Niezawodność

2021

tom Vol. 23, no. 3

575--585

In this paper several statistical learning algorithms are used to predict the maximal length of fatigue cracks based on a sample composed of 31 observations. The small-data regime is still a problem for many professionals, especially in the areas where failures occur rarely. The analyzed object is a high-pressure Nozzle of a heavy-duty gas turbine. Operating parameters of the engines are used for the regression analysis. The following algorithms are used in this work: multiple linear and polynomial regression, random forest, kernel-based methods, AdaBoost and extreme gradient boosting and artificial neural networks. A substantial part of the paper provides advice on the effective selection of features. The paper explains how to process the dataset in order to reduce uncertainty; thus, simplifying the analysis of the results. The proposed loss and cost functions are custom and promote solutions accurately predicting the longest cracks. The obtained results confirm that some of the algorithms can accurately predict maximal lengths of the fatigue cracks, even if the sample is small.

ARTIFICIAL GRAMMAR LEARNING IN ANIMALS

75%

Tauzin T. M.

Magyar Pszichologiai Szemle (Hungarian Psychological Review)

2010

tom 65

nr 2

389-412

In this article I review the artificial grammar learning experiments in comparative psychology from the last one and a half decades. Artificial grammar learning in animals will be discussed targeting statistical learning, algebraic learning and hierarchical rule learning. Furthermore, I propose a new model which can explain some of the contradictory results in this field. I argue that social species necessarily have refined contingency detection mechanisms, which allows them to monitor contingencies between various stimuli. This mechanism and a repetition detector, which is sensitive to identical successive stimuli, create abstract contingency-profiles. These can help to recognize repetitive abstract signs, thus a part of algebraic or hierarchical rules.

Three-way learnability: A learning theoretic perspective on Three-way Decision

51%

Campagner A. , Ciucci D.

Annals of Computer Science and Information Systems

2022

tom Vol. 30

243--246

In this article we study the theoretical properties of Three-way Decision (TWD) based Machine Learning, from the perspective of Computational Learning Theory, as a first attempt to bridge the gap between Machine Learning theory and Uncertainty Representation theory. Drawing on the mathematical theory of orthopairs, we provide a generalization of the PAC learning framework to the TWD setting, and we use this framework to prove a generalization of the Fundamental Theorem of Statistical Learning. We then show, by means of our main result, a connection between TWD and selective prediction.