Non-well-founded set theory Recursion Self-dissimilarity Self-reference Self-replication Self-similarity of network data analysis Self-similar process Teragon Tessellation...

In computer networks, self-similarity is a feature of network data transfer dynamics. When modeling network data dynamics the traditional time series models...

In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the...

Cluster analysis or clustering is the data analyzing technique in which task of grouping a set of objects in such a way that objects in the same group...

distributed memory Topological data analysis Kohonen, Teuvo (January 2013). "Essentials of the self-organizing map". Neural Networks. 37: 52–65. doi:10.1016/j...

Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures...

of semantic networks has been created for specific use. For example, in 2008, Fawsy Bendeck's PhD thesis formalized the Semantic Similarity Network (SSN)...

In the context of neural networks, self-supervised learning aims to leverage inherent structures or relationships within the input data to create meaningful...

Intelligence: from data analysis to generative AI. Intellisemantic Editions. ISBN 978-8-8947-8760-3. Ganesan N (2010). "Application of Neural Networks in Diagnosing...

modifying this result with network analysis techniques is also common. The choice of similarity measure depends on the type of data being clustered and the...

comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) via unsupervised learning...

explosion of publicly available high throughput biological data, the analysis of molecular networks has gained significant interest. The type of analysis in...

the network’s function and structure. One popular approach to making use of medoids in social network analysis is to compute a distance or similarity metric...

relational similarities between words. Siamese Networks: Siamese networks are a type of neural network architecture commonly used for similarity-based embedding...

unlabeled data. Other frameworks in the spectrum of supervisions include weak- or semi-supervision, where a small portion of the data is tagged, and self-supervision...

component analysis, matrix factorization, and various forms of clustering. In self-supervised feature learning, features are learned using unlabeled data like...

important feature of ASNN is the possibility to interpret neural network results by analysis of correlations between data cases in the space of models. A physical...

Proactive learning Proximal gradient methods for learning Semantic analysis Similarity learning Sparse dictionary learning Stability (learning theory) Statistical...

contrasting spectra via network analysis. Scoring functions are used to determine the similarity between pairs of fragment spectra as part of these processes...

Incremental Data Allocation". arXiv:1601.00024 [cs.LG]. Xu et al. "SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)" Proceedings of the...

networks. The goal is that semantically similar data items receive feature vectors close to each other. Vector databases can be used for similarity search...

brought to the fore by Benoit Mandelbrot based on his 1967 paper on self-similarity in which he discussed fractional dimensions. In that paper, Mandelbrot...

The generative network generates candidates while the discriminative network evaluates them. The contest operates in terms of data distributions. Typically...

process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods...

methods require only a user-specified kernel, i.e., a similarity function over all pairs of data points computed using inner products. The feature map...

Self-similar processes are stochastic processes satisfying a mathematically precise version of the self-similarity property. Several related properties...

successive magnifications of the Mandelbrot set. This exhibition of similar patterns at increasingly smaller scales is called self-similarity, also known as expanding...

domains such as numerical analysis, sampling, combinatorics, machine learning, data mining and databases. The common theme of these problems is that when...

"The influence of pattern similarity and transfer learning on the base perceptron training." (original in Croatian) Proceedings of Symposium Informatica...

several types of traffic behavior, that can have significant impact on network performance, were discovered: long-range dependence, self-similarity and, more...