Skip to content
General Erlang-coded Reinforcement-Learning paradigm for Machine Learning [THESIS]
Erlang Java
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
DataMiningMetaFLIP
GUIErlang
IFPER
README.md

README.md

gErl

##General Erlang-coded Reinforcement-Learning paradigm for Machine Learning [THESIS]

###APROACH Erlang-coded general learning system. With this system, we push forward the idea of machine learning systems for which the operators can be modified and finetuned 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 affect 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 defined 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.

REPORTS AND ARTICLES RELATED TO THE PROJECT

  • Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo, M. José Ramírez-Quintana, On the definition of a general learning system with user-defined operators , CoRR abs/1311.4235 2013

  • Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo, M. José Ramírez-Quintana, Policy Reuse in a General Learning Framework , Fifteenth Spanish Conference for Artificial Intelligence, CAEPIA 2013, ISBN 978-84-695-8348-7, pages 183-190, 2013.

  • Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo, M. José Ramírez-Quintana, Incremental learning with state features in gErl , In Alicia Troncoso, Jose C. Riquelme (eds) V Symposium on Theory and Applications of Data Mining, TAMIDA 2013 (CEDI 2013), ISBN 978-84-695-8348-7, pages 183-190, 2013.

  • Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo, M. José Ramírez-Quintana, Learning with cofigurable operators and RL-based heuristics , In A. Appice et al.,editor, New Frontiers in Mining Complex Patterns , volume 7765 of Lecture Notes in Computer Science (LNCS), pages1-16. Springer-Verlag, 2013.

  • Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo, M. José Ramírez-Quintana, Towards the definition of learning systems with configurable operators and heuristics , in procedings of the 1st Workshop on New Frontiers in Mining Complex Patterns (NFMCP Workshop held at ECML-PKDD 2012) ,Bristol, UK, September 2012.

###HOW TO

Documentation: www.dsic.upv.es/~fmartinez/systems/Readme_gErl.pdf

Technology:

Erlang
Java
Weka Libraries
You can’t perform that action at this time.