Zobrazeno 1 - 10
of 903
pro vyhledávání: '"LIKAS, A."'
Autor:
Chasani, Paraskevi, Likas, Aristidis
Unimodality constitutes a key property indicating grouping behavior of the data around a single mode of its density. We propose a method that partitions univariate data into unimodal subsets through recursive splitting around valley points of the dat
Externí odkaz:
http://arxiv.org/abs/2412.15894
Approximating solutions of ordinary and partial differential equations constitutes a significant challenge. Based on functional expressions that inherently depend on neural networks, neural forms are specifically designed to precisely satisfy the pre
Externí odkaz:
http://arxiv.org/abs/2404.19454
Unsupervised learning has gained prominence in the big data era, offering a means to extract valuable insights from unlabeled datasets. Deep clustering has emerged as an important unsupervised category, aiming to exploit the non-linear mapping capabi
Externí odkaz:
http://arxiv.org/abs/2402.00608
Silhouette coefficient is an established internal clustering evaluation measure that produces a score per data point, assessing the quality of its clustering assignment. To assess the quality of the clustering of the whole dataset, the scores of all
Externí odkaz:
http://arxiv.org/abs/2401.05831
Estimating the number of clusters k while clustering the data is a challenging task. An incorrect cluster assumption indicates that the number of clusters k gets wrongly estimated. Consequently, the model fitting becomes less important. In this work,
Externí odkaz:
http://arxiv.org/abs/2312.11323
Autor:
Kolyvakis, Prodromos, Likas, Aristidis
Unimodality, pivotal in statistical analysis, offers insights into dataset structures and drives sophisticated analytical procedures. While unimodality's confirmation is straightforward for one-dimensional data using methods like Silverman's approach
Externí odkaz:
http://arxiv.org/abs/2311.16614
Autor:
Chasani, Paraskevi1 (AUTHOR) pchasani@cs.uoi.gr, Likas, Aristidis1 (AUTHOR) arly@cs.uoi.gr
Publikováno v:
Information (2078-2489). Nov2024, Vol. 15 Issue 11, p704. 17p.
Autor:
Vardakas, Georgios, Likas, Aristidis
The $k$-means algorithm is a prevalent clustering method due to its simplicity, effectiveness, and speed. However, its main disadvantage is its high sensitivity to the initial positions of the cluster centers. The global $k$-means is a deterministic
Externí odkaz:
http://arxiv.org/abs/2211.12271
Autor:
Adam, Stavros P., Likas, Aristidis C.
Neural classifiers are non linear systems providing decisions on the classes of patterns, for a given problem they have learned. The output computed by a classifier for each pattern constitutes an approximation of the output of some unknown function,
Externí odkaz:
http://arxiv.org/abs/2204.02241
Publikováno v:
Journal of Intelligent Systems, Vol 8, Iss 1-2, Pp 55-80 (1998)
Externí odkaz:
https://doaj.org/article/4388835b6ee74888877294d18693e0d3