Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "search and score" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Falcon optimization algorithm for bayesian network structure learning
Autorzy:
Kareem, Shahab Wahhab
Okur, Mehmet Cudi
Powiązania:
https://bibliotekanauki.pl/articles/2097968.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
Bayesian network
global search
falcon optimization algorithm
structure learning
search and score
Opis:
In machine-learning, some of the helpful scientific models during the production of a structure of knowledge are Bayesian networks. They can draw the relationships of probabilistic dependency among many variables. The score and search method is a tool that is used as a strategy for learning the structure of a Bayesian network. The authors apply the falcon optimization algorithm (FOA) to the learning structure of a Bayesian network. This paper has employed reversing, deleting, moving, and inserting to obtain the FOA for approaching the optimal solution of a structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is associated with pigeon-inspired optimization, greedy search, and simulated annealing that apply the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques by utilizing several benchmark data sets. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values).
Źródło:
Computer Science; 2021, 22 (4); 553--569
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Learning causal theories with non-reversible MCMC methods
Autorzy:
Krajewska, Antonina
Powiązania:
https://bibliotekanauki.pl/articles/2183467.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
Bayesian inference
causal systems
directed acyclic graph
MCMC
non-reversible Markov processes
search and score methods
Opis:
Causal laws are defined in terms of concepts and the causal relations between them. Following Kemp et al. (2010), we investigate the performance of the hierarchical Bayesian model, in which causal systems are represented by directed acyclic graphs (DAGs) with nodes divided into distinct categories. This paper presents two non-reversible search and score algorithms (Q1 and Q2) and their application to the causal learning system. The algorithms run through the pairs of class-assignment vectors and graph structures and choose the one which maximizes the probability of given observations. The model discovers latent classes in relational data and the number of these classes and predicts relations between objects belonging to them. We evaluate its performance on prediction tasks from the behavioural experiment about human cognition. Within the discussed approach, we solve a simplified prediction problem when object classification is known in advance. Finally, we describe the experimental procedure allowing in-depth analysis of the efficiency and scalability of both search and score algorithms.
Źródło:
Control and Cybernetics; 2021, 50, 3; 323--361
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies