Search results for "Algoritmi"

Stereotaktisten annossuunnitelmien verifiointi Compass-järjestelmällä

2016

Exact extension of the DIRECT algorithm to multiple objectives

2019

The direct algorithm has been recognized as an efficient global optimization method which has few requirements of regularity and has proven to be globally convergent in general cases. direct has been an inspiration or has been used as a component for many multiobjective optimization algorithms. We propose an exact and as genuine as possible extension of the direct method for multiple objectives, providing a proof of global convergence (i.e., a guarantee that in an infinite time the algorithm becomes everywhere dense). We test the efficiency of the algorithm on a nonlinear and nonconvex vector function. peerReviewed

A Serendipity-Oriented Greedy Algorithm for Recommendations

2017

Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected, i.e. serendipitous items. In this paper, we propose a serendipity-oriented algorithm, which improves serendipity through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most …

Recommending Serendipitous Items using Transfer Learning

2018

Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous recommendations is difficult due to the lack of training data. To the best of our knowledge, there are many large datasets containing relevance scores (relevance oriented) and only one publicly available dataset containing a relatively small number of serendipity scores (serendipity oriented). This limits the learning capabilities of serendipity oriented algorithms. Therefore, in the absence of any known deep learning algorithms for recommend…

Scalable implementation of dependence clustering in Apache Spark

2017

This article proposes a scalable version of the Dependence Clustering algorithm which belongs to the class of spectral clustering methods. The method is implemented in Apache Spark using GraphX API primitives. Moreover, a fast approximate diffusion procedure that enables algorithms of spectral clustering type in Spark environment is introduced. In addition, the proposed algorithm is benchmarked against Spectral clustering. Results of applying the method to real-life data allow concluding that the implementation scales well, yet demonstrating good performance for densely connected graphs. peerReviewed

Peruslaskutoimitusten algoritmien hallinta toisen asteen ammatillisten opintojen alussa teknisillä aloilla

2004

Sparse nonnegative tensor decomposition using proximal algorithm and inexact block coordinate descent scheme

2021

Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extracted components are nonnegative and usually sparse. Nevertheless, the sparsity is only a side effect and cannot be explicitly controlled without additional regularization. In this paper, we investigated the nonnegative CANDECOMP/PARAFAC (NCP) decomposition with the sparse regularization item using l1-norm (sparse NCP). When high sparsity is imposed, the factor matrices will contain more zero components and will not be of full column rank. Thus, the sparse NCP is prone to rank deficiency, and the algorithms of sparse NCP may not converge. In this paper, we proposed a novel model of sparse NCP w…

Probabilistic analysis of sorting algorithms : lecture notes

2004

PACKET SCHEDULING AND PRICING BASED ON INFLICTED DELAY

2006

An adaptive packet scheduling method is presented in this paper. The adaptive weights of a scheduler are chosen based on maximizing the revenue of the network service provider. The pricing scenario is based on the delay that a connection will inflict to other connections. The features of the adaptive weight updating algorithm are simulated, analyzed and compared to a constant weight algorithm. peerReviewed

Ceļu satiksmes automatizācijas efektivitāte Rīgas infrastruktūrā

2021

Darba mērķis ir atrast efektīvu algoritmu automātiskai luksoforu pārslēgšanas ciklu pārvaldībai Rīgas krustojumos. Mērķa sasniegšanai izmanto reālu statistiku par transportlīdzekļu un gājēju skaitu konkrētos pilsētas krustojumos. Uz šīs statistikas pamata paredzēts veikt simulācijas, kuru rezultātā iespējama konkrētu risinājumu efektivitātes izvērtēšana. Risinājumu mērķis ir samazināt pārvietošanās aizturi visiem ceļu satiksmes dalībniekiem, priekšroku dodot gājējiem.