Wyniki wyszukiwania - BazTech

1

Partial (Neighbourhood) Singleton Arc Consistency for Constraint Satisfaction Problems

Wallace Richard J.

Fundamenta Informaticae

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2020

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Vol. 174, nr 3/4

311--344

EN

Algorithms based on singleton arc consistency (SAC) show considerable promise for improving backtrack search algorithms for constraint satisfaction problems (CSPs). The drawback is that even the most efficient of them is still comparatively expensive. Even when limited to preprocessing, they give overall improvement only when problems are quite difficult to solve with more typical procedures such as maintained arc consistency (MAC). The present work examines a form of partial SAC and neighbourhood SAC (NSAC) in which a subset of the variables in a CSP are chosen to be made SAC-consistent or neighbourhood-SAC-consistent. Such consistencies, despite their partial character, are still well-characterized in that algorithms have unique fixpoints. Heuristic strategies for choosing an effective subset of variables are described and tested, the best being choice by highest degree and a more complex strategy of choosing by constraint weight after random probing. Experimental results justify the claim that these methods can be nearly as effective as the corresponding full version of the algorithm in terms of values discarded or problems proven unsatisfiable, while significantly reducing the effort required to achieve this.

2

Porównanie algorytmów wyszukiwania Google i Yandex

Bihun O., Miłosz M.

Journal of Computer Sciences Institute

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2017

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Vol. 4

128--130

PL

W artykule przedstawiono rezultaty analizy porównawczej wyszukiwarek Google i Yandex. Analizę przeprowadzono pod kątem szybkości wyszukiwania oraz liczby znalezionych wyników. Przy pomocy metody kryteriów ważonych porównano Google i Yandex dla wyszukiwania w języku angielskim i rosyjskim. Analiza wielokryterialna pozwoliła wskazać wyszukiwarkę o szybszych i wydajniejszych algorytmach.

EN

The article presents the results of a comparative analysis Google’s and Yandex’s search algorithms. The analysis was made from the point of view of search speed and number of results. Google and Yandex were compared for search in Russian and English language, using the method of weighted criteria. Multi-criteria analysis allowed to find faster and efficient search engine.

3

Search Result Clustering Based on Query Context

Meina M., Nguyen H. S.

Fundamenta Informaticae

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2015

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Vol. 137, nr 2

273--290

EN

This paper introduces a novel, interactive and exploratory, approach to information retrieval (search engines) based on clustering. Presented method allows users to change the clustering structure by applying a free-text clustering context query that is treated as a criterion for documentto- cluster allocation. Exploration mechanisms are delivered by redefining the interaction scenario in which the user can interact with data on the level of topic discovery or cluster labeling. In this paper, the presented idea is realized by a graph structure called the Query-Summarize Graph. This data structure is useful in the definition of the similarity measure between the snippets as well as in the snippet clustering algorithm. The experiments on real-world data are showing that the proposed solution has many interesting properties and can be an alternative approach to interactive information retrieval.

4

Real-time pathfinding algorithms in practical application

Pochmara J, Grygiel W

Elektronika : konstrukcje, technologie, zastosowania

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2013

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Vol. 54, nr 12

9-13

EN

Due to the fact that the A* algorithm is very flexible, can be used in a variety of situations, it became the main algorithm used in our study. Its biggest drawback is the need for large amounts of memory to store all the surveyed points. This problem greatly increases with the increase in the study area. However, the A* algorithm allows the robots to make efficient decisions on how to move from the starting to the ending point. Because of this, the A* algorithm should be taken into account as an option for pathfinding for intelligent robots.

PL

Ze względu na to, że Algorytm A* jest bardzo elastyczny, można go stosować różnych sytuacjach, stał się on głównym algorytmem wykorzystywanym podczas naszych badań. Jego największą wadą jest potrzeba dużej ilości pamięci w celu zapamiętania wszystkich zbadanych punktów. Ten problem znacznie się nasila wraz ze wzrostem badanego obszaru. Jednak algorytm A* pozwala robotom na podejmowanie sprawnych decyzji co do sposobu poruszania się od punktu startowego do końcowego. Z tego powodu warto brać algorytm A* pod uwagę jako opcję dla poszukiwania dróg przez inteligentne roboty.

5

Efficient Search Methods for Statistical Dependency Rules

Hämäläinen W.

Fundamenta Informaticae

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2011

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Vol. 113, nr 2

117-150

EN

Dependency analysis is one of the central problems in bioinformatics and all empirical science. In genetics, for example, an important problem is to find which gene alleles are mutually dependent or which alleles and diseases are dependent. In ecology, a similar problem is to find dependencies between different species or groups of species. In both cases a classical solution is to consider all pairwise dependencies between single attributes and evaluate the relationships with some statistical measure like the χ2-measure. It is known that the actual dependency structures can involve more attributes, but the existing computational methods are too inefficient for such an exhaustive search. In this paper, we introduce efficient search methods for positive dependencies of the form X → A with typical statistical measures. The efficiency is achieved by a special kind of a branch-andbound search which also prunes out redundant rules. Redundant attributes are especially harmful in dependency analysis, because they can blur the actual dependencies and even lead to erroneous conclusions. We consider two alternative definitions of redundancy: the classical one and a stricter one. We improve our previous algorithm for searching for the best strictly non-redundant dependency rules and introduce a totally new algorithm for searching for the best classically non-redundant rules. According to our experiments, both algorithms can prune the search space very efficiently, and in practice no minimum frequency thresholds are needed. This is an important benefit, because biological data sets are typically dense, and the alternative search methods would require too large minimum frequency thresholds for any practical purpose.

6

Search-based approach to multilayer perceptron training

Kordos M.

Studia Informatica

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2005

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Vol. 26, nr 1

29-43

EN

The paper presents two search-based algorithms for MLP training; numerical gradient and yariable step search algorithm. The advantages of the methods comparing to analytical gradient-based algorithms include Iow memory reąuirements, the algorithm simplicity and determining morę optimal next step direction by direct access to the influence of hidden layer weights on nerwork error.

PL

Artykuł przedstawia dwie metody uczenia sieci MLP oparte na . przeszukiwaniu: gradient numeryczny i metodę zmiennego kroku przeszukiwania (VSS). Zaletą tych metod są małe wymagania pamięciowe, prosta budowa algorytmu t v oraz dokładniejsze określenie kierunku następnego kroku przez bezpos'rednie określanie wpływu wartości wag warstwy ukrytej na błąd sieci.