The discovery of possible interactions with objects is a vital part of an exploration task for robots. An important subset of these possible interactions are affordances. Affordances describe what a specific object can afford to specific agent, based on the capabilities of the agent and the properties of the object in relation to the agent. For example, a chair affords a human to be sat-upon, i...

The aim of this project is to investigate and develop new machine learning techniques which can be applied to agent based applications such as those that assist in information filtering. The motivation for this work emerged from reviewing the literature on Interface Agents, and applying existing machine learning techniques to an intelligent interface agent which filtered incoming electronic mai...

Instance-based learning algorithms are widely used due to their capacity to approximate complex target functions; however, the performance of this kind of algorithms degrades significantly in the presence of irrelevant features. This paper introduces a new noise tolerant instance-based learning algorithm, called WIB-K, that uses one or more weights, per feature per class, to classify integer-va...

This paper introduces a new classification algorithm of the instance-based learning type. Training records are converted into patterns associated with a known class label, and stored permanently into a trie1-like tree structure along with other helpful information. Classifying new records is done selecting from the trie two best patterns as solutions hypotheses. Best pattern selection is done u...

This paper describes a HMM-based chunk tagger and its extensions used in KRDL for the shared task of CoNLL'2000. Compared with standard HMM-based tagger, this tagger incorporates more contextual information into a lexical entry. Moreover, an error-driven learning approach is adopted to decrease the memory requirement. It keeps only positive lexical entries which contribute to the error reductio...

The processing of data streams in general and the mining of such streams in particular have recently attracted considerable attention in various research fields. A key problem in stream mining is to extend existing machine learning and data mining methods so as to meet the increased requirements imposed by the data stream scenario, including the ability to analyze incoming data in an online, in...

In this work we focus on the determination of the relative distributions of young, intermediate-age and old populations of stars in galaxies. Starting from a grid of theoretical population synthesis models we constructed a set of model galaxies with a distribution of ages, metallicities and intrinsic reddening. Using this set we have explored a new fitting method that presents several advantage...

This paper addresses the problem of dealing with set-valued attributes in the lazy learning context. This type of attribute is present in various domains, yet the instance-based learning tools do not provide a representation for them. To solve this problem, we present a proposal for the treatment of the sets in the context of the k-NN algorithm through an extension to HEOM distance. Experiments...

[3] Rakesh Agrawal and Arun Swami. A one-pass space-efficient algorithm for finding quantiles. A one-pass algorithm for accurately estimating quantiles for disk-resident data. [8] Jürgen Beringer and Eyke Hüllermeier. An efficient algorithm for instance-based learning on data streams.

We explore the application of machine learning techniques to the problem of content-based image retrieval (CBIR). Unlike most existing CBIR systems in which only global information is used or in which a user must explicitly indicate what part of the image is of interest, we apply the multiple-instance (MI) learning model to use a small number of training images to learn what images from the dat...