Inductive Reasoning with Multi-dimensional Concepts
Bibliographic Data
| ID | 8397591 |
|---|---|
| Authors | Marta Sznajder (0000-0001-7209-0349, Munich School of Philosophy, corresponding author) |
| Year | 2021 |
| Volume | 72 |
| Issue | 2 |
| Pages | 465-484 |
| Publication date | 2021-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The British Journal for the Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0007-0882 • E-ISSN: 1464-3537 |
| Publisher | Oxford University Press (PUBLISHER • GB) |
| DOI | 10.1093/bjps/axz012 |
| OpenAlex | W2998105637 |
| Language | EN |
| Citations received | 1 |
| References cited | 21 |
Attribute spaces are a type of conceptual spaces which Carnap introduced in his late basic system of inductive logic. This article shows how to extend Carnap’s use of them into a full model of inductive reasoning with geometrically represented concepts, extending my earlier work (Sznajder [2017]). The proposed model draws on Bayesian non-parametric techniques in order to define a probability distribution over the attribute space and a way of updating it with data. The model is another example of conceptual and formal continuity of Carnapian inductive logic with subsequent developments in statistics.
Inductive bias · Inductive reasoning · Multi-task learning · Parametric statistics · Space (punctuation) · Statistics · Artificial Intelligence · Bayesian Modeling and Causal Inference · Biomedical Text Mining and Ontologies · Computer Science · Mathematics · Rough Sets and Fuzzy Logic
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| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |