We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under our approach, manifolds in high-dimensional spaces are inferred by estimating geometric relationships among the input instances. Unlike conventional manifold learning, we do not perform dimensionality reduction, but i...

We describe results on pitch accent placement in Dutch text obtained with a memory-based learning approach. The training material consists of newspaper texts that have been prosodically annotated by humans, and subsequently enriched with linguistic features and informational metrics using generally available, lowcost, shallow, knowledge-poor tools. We report on the effects of context-modelling ...

Social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. However, much of the data from social media are images, which are uploaded in a rate that makes it impossible for human beings to analyze them. Despite the many works on image analysis, there are no fire detection studies on social media. To fill this gap,...

Nearest neighbor and instance-based learning techniques typically handle continuous and linear input values well, but often do not handle symbolic input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between symbolic attribute values, but it largely ignores continuous attributes, using discretization to map continuous values into symb...

This paper describes the UNED’s Online Reputation Monitoring Team participation at RepLab 2013 [3]. Several approaches were tested: first, an instance-based learning approach that uses Heterogeneity Based Ranking to combine seven different similarity measures was applied for all the subtasks. The filtering subtask was also tackled by automatically discovering filter keywords: those whose presen...

The article describes a method combining two widely-used empirical approaches: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically induced during the learning p...

A new approach for predicting execution times of parallel applications is presented. The main goal is to improve decision making in parallel systems, providing the system scheduler with knowledge about parallel applications. A model is produced searching for similar applications on based experience, allowing effortless knowledge updating when new information occurs. Workload traces from three c...

This paper describes an experiment and a computational cognitive model involving a spatial orientation task. The experiment tests participants’ ability to identify their location on a map, given a view of the space. The model performs this task by applying an instantiation of a general strategy that has been shown to be effective in other spatial orientation tasks. It uses perceptual grouping t...