4 citations · 5 across the 5 of their papers we have counts for
5 papers
A Recursive Bateson-Inspired Model for the Generation of Semantic Formal Concepts from Spatial Sensory Data
Jaime de Miguel-Rodriguez, Fernando Sancho-Caparrini
Neural-symbolic approaches to machine learning incorporate the advantages from both connectionist and symbolic methods. Typically, these models employ a first module based on a neu…
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling
Pedro Almagro-Blanco, Fernando Sancho-Caparrini
Network embedding techniques inspired by word2vec represent an effective unsupervised relational learning model. Commonly, by means of a Skip-Gram procedure, these techniques learn…
Semantic Preserving Embeddings for Generalized Graphs
Pedro Almagro-Blanco, Fernando Sancho-Caparrini
A new approach to the study of Generalized Graphs as semantic data structures using machine learning techniques is presented. We show how vector representations maintaining semanti…
Induction of Decision Trees based on Generalized Graph Queries
Pedro Almagro-Blanco, Fernando Sancho-Caparrini
Usually, decision tree induction algorithms are limited to work with non relational data. Given a record, they do not take into account other objects attributes even though they ca…
Generalized Graph Pattern Matching
Pedro Almagro-Blanco, Fernando Sancho-Caparrini
Most of the machine learning algorithms are limited to learn from flat data: a recordset with prefixed structure. When learning from a record, these types of algorithms don't take…