Languages for Learning and Mining
Authors: Luc De Raedt
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This note provides a gentle introduction to three types of languages that support machine learning and data mining: inductive query languages, which extend database query languages with primitives for mining and learning, modelling languages, which allow to declaratively specify and solve mining and learning problems, and programming languages, that support the learning of functions and subroutines. |
| Researcher Affiliation | Academia | Luc De Raedt KU Leuven, Department of Computer Science Celestijnenlaan 200A, POBox 2402 3001 Heverlee, Belgium |
| Pseudocode | Yes | var set of 1..N: Itemset; array[int] of set of 1..N: D_fraud; array[int] of set of 1..N: D_ok; constraint card(cover(Itemset,D_fraud)) > 5 ; % Optimisation function var int: Score = card(cover(Items, D_fraud)) card(cover(Items, D_ok)); solve maximize Score :: itemset_search(Items); |
| Open Source Code | No | The paper introduces concepts and existing languages; it does not provide source code for a new method presented in this paper. |
| Open Datasets | No | The paper uses an illustrative 'Beer table' (Table 1) but does not provide access information for it as it's an example, not a dataset for empirical evaluation. |
| Dataset Splits | No | This paper is a conceptual overview and does not describe experiments with dataset splits. |
| Hardware Specification | No | This paper is a theoretical discussion of programming languages and does not detail any experimental hardware specifications. |
| Software Dependencies | No | While various software packages and languages are mentioned (e.g., Scikit, Weka, Orange, Knime, Mini Zinc), no specific version numbers are provided for reproducibility. |
| Experiment Setup | No | This paper is a conceptual overview and does not describe an experimental setup with hyperparameters or system-level training settings. |