Ampliando os limites do aprendizado indutivo de máquina através das abordagens construtiva e relacional. / Extending the limits of inductive machine learning through constructive and relational approaches.
AUTOR(ES)
Maria do Carmo Nicoletti
DATA DE PUBLICAÇÃO
1994
RESUMO
This work investigates Inductive Machine Learning as a function of the description languages employed to express instances, concepts and domain theory. The enlargement of the representational power of propositional learning methods is approached via constructive induction, in the domain of boolean functions, through the proposal of a bias for composing attributes, namely, the bias root-fringe. Experimental evaluation of root-fringe, as well as other biases for constructing new attributes was conducted and the results analyzed. Two pruning methods for decision trees were evaluated in an environment of constructive induction and the results discussed. Due to the limitations of propositional learning, ways of enlarging the limits of the learning process were investigated through enlarging the representational power of the description languages. It was chosen Inductive Logic Programming - ILP - that is an inductive learning paradigm that uses restrictions of First Order Logic as description languages. Learning using ILP is only feasible when the languages are restricted and are strongly controlled; otherwise, learning in ILP becomes undecidible. Research work in ILP was directed towards restricting domain theory and hypotheses description languages. Three algorithms that "translate" the intentional expression of a domain theory into its extensional expression are presented. The implementations of two of them are discussed. The implementations gave rise to two experimental learning environments: the propositional environment, which includes the constructive environment, and the relational environment.
ASSUNTO(S)
programação lógica indutiva aprendizado construtivo aprendizado de máquina machine learning constructive learning inductive logic programming
Documentos Relacionados
- Machine learning in complex networks
- "New approaches in machine learning for rule generation, class imbalance and rankings"
- APRENDIZADO DE MÁQUINA PARA O PROBLEMA DE SENTIMENT CLASSIFICATION
- Detecção autônoma de intrusões utilizando aprendizado de máquina
- Relational reinforcement learning to control sociable robots
