Projects per year
Abstract
Machine and statistical learning is about constructing models from data. Data is usually understood as a set of records, a database. Nevertheless, databases are not static but change over time. We can understand this as follows: there is a space of possible databases and a database during its lifetime transits this space. Therefore, we may consider transitions between databases, and the database space. NoSQL databases also fit with this representation. In addition, when we learn models from databases, we can also consider the space of models. Naturally, there are relationships between the space of data and the space of models. Any transition in the space of data may correspond to a transition in the space of models. We argue that a better understanding of the space of data and the space of models, as well as the relationships between these two spaces is basic for machine and statistical learning. The relationship between these two spaces can be exploited in several contexts as, e.g., in model selection and data privacy. We consider that this relationship between spaces is also fundamental to understand generalization and overfitting. In this paper, we develop these ideas. Then, we consider a distance on the space of models based on a distance on the space of data. More particularly, we consider distance distribution functions and probabilistic metric spaces on the space of data and the space of models. Our modelization of changes in databases is based on Markov chains and transition matrices. This modelization is used in the definition of distances. We provide examples of our definitions.
Original language  English 

Pages (fromto)  321332 
Number of pages  12 
Journal  Progress in Artificial Intelligence 
Volume  10 
Issue number  3 
DOIs  
Publication status  Published  Sept 2021 
Keywords
 Hypothesis space
 Machine and statistical learning models
 Probabilistic metric spaces
 Space of data
 Space of models
Fingerprint
Dive into the research topics of 'The space of models in machine learning: using Markov chains to model transitions'. Together they form a unique fingerprint.Projects
 1 Finished

Plataforma segura crowd2crowd para aplicaciones de transporte inteligente oportunistas y descentralizadas
Robles Martinez, S., Borrego Iglesias, C., Perez Sola, C., Sánchez Carmona, A., Borrell Viader, J., Marti Escale, R. & Navarro Arribas, G.
1/01/18 → 30/09/21
Project: Research Projects and Other Grants