Learning with con gurable operators and RL-based heuristics
In this paper, we push forward the idea of machine learning systems for which the operators can be modi ed and netuned for each problem. This allows us to propose a learning paradigm where users can write (or adapt) their operators, according to the problem, data representation and the way the infor...
| Autores: | , , , |
|---|---|
| Formato: | capítulo de livro |
| Fecha de publicación: | 2013 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/37322 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/37322 |
| Access Level: | acceso abierto |
| Palavra-chave: | Machine learning operators Complex data Heuristics Inducting programming Reinforcement learning Erlang LENGUAJES Y SISTEMAS INFORMATICOS |
| Resumo: | In this paper, we push forward the idea of machine learning systems for which the operators can be modi ed and netuned for each problem. This allows us to propose a learning paradigm where users can write (or adapt) their operators, according to the problem, data representation and the way the information should be navigated. To achieve this goal, data instances, background knowledge, rules, programs and operators are all written in the same functional language, Erlang. Since changing operators a ect how the search space needs to be explored, heuristics are learnt as a result of a decision process based on reinforcement learning where each action is de ned as a choice of operator and rule. As a result, the architecture can be seen as a `system for writing machine learning systems' or to explore new operators. |
|---|