The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit arbitrary spike trains in response to given synaptic inputs. Recent years, the supervised learning algorithms based on synaptic plasticity have developed rapidly. As one of the most efficient supervised learning algorithms, the remote supervised method (ReSuMe) uses the conventional pair-ba...

The aim of statistical relational learning is to learn statistical models from relational or graph-structured data. Three main statistical relational learning paradigms include weighted rule learning, random walks on graphs, and tensor factorization. These paradigms have been mostly developed and studied in isolation for many years, with few works attempting at understanding the relationship am...

As the number of web pages increases, search for useful information by users on web sites will become more significant. By determining the similarity of web pages, search quality can be improved; hence, users can easily find their relevant information. In this paper, distributed learning automata and probabilistic grammar were used to propose a new hybrid algorithm in order to specify the simil...

In this work, we conduct a preliminary study considering a fuzzy rule-based multiclassification system design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). This advanced method serves as the fuzzy classification rule learning algorithm to derive the component classifiers considering bagging combined with feature selection. We develop a study on the use of both bagging and...

NeuroFAST is an on-line fuzzy modeling learning algorithm, featuring high function approximation accuracy and fast convergence. It is based on a first-order Takagi-Sugeno-Kang (TSK) model, where the consequence part of each fuzzy rule is a linear equation. Structure identification is performed by a fuzzy adaptive resonance theory (ART)-like mechanism, assisted by fuzzy rule splitting and adding...

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean...

This document describes a rule learning framework called SeCo (derived from Separate-and-Conquer). This framework uses building blocks to specify each component that is needed to build up a configuration for a rule learner. It is based on a general separate-and-conquer algorithm. The framework allows to configurate any given rule learner that fulfills some properties. These are mainly that it e...

In this work, we conduct a study considering a fuzzy rule-based multiclassification system design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). This advanced method serves as the fuzzy classification rule learning algorithm to derive the component classifiers considering bagging and feature selection. We develop an exhaustive study on the potential of bagging and feature ...

In many planning domains, it is possible to define and learn good rules for reactively selecting actions. This has lead to work on learning rule-based policies as a form of planning control knowledge. However, it is often the case that such learned policies are imperfect, leading to planning failure when they are used for greedy action selection. In this work, we seek to develop a more robust f...