Results 1  10
of
28
A Survey of Kernels for Structured Data
"... Kernel methods in general and support vector machines in particular have been successful in various learning tasks on data represented in a single table. Much 'realworld ' data, however, is structured it has no natural representation in a single table. Usually, to apply kernel methods to 'realworl ..."
Abstract

Cited by 113 (3 self)
 Add to MetaCart
Kernel methods in general and support vector machines in particular have been successful in various learning tasks on data represented in a single table. Much 'realworld ' data, however, is structured it has no natural representation in a single table. Usually, to apply kernel methods to 'realworld' data, extensive preprocessing is performed toembed the data into areal vector space and thus in a single table. This survey describes several approaches ofdefining positive definite kernels on structured instances directly.
Relational reinforcement learning: An overview
 In Proceedings of the ICML’04 Workshop on Relational Reinforcement Learning
, 2004
"... Relational reinforcement learning (RRL) is both a young and an old eld. In this paper, we trace the history of the eld to related disciplines, outline some current work and promising new directions, and survey the research issues and opportunities that lie ahead. 1. ..."
Abstract

Cited by 31 (3 self)
 Add to MetaCart
Relational reinforcement learning (RRL) is both a young and an old eld. In this paper, we trace the history of the eld to related disciplines, outline some current work and promising new directions, and survey the research issues and opportunities that lie ahead. 1.
Hierarchical MultiClassification
, 2002
"... The problem of hierarchical multiclassification is considered. ..."
Abstract

Cited by 22 (6 self)
 Add to MetaCart
The problem of hierarchical multiclassification is considered.
Counting Pedestrians in Video Sequences Using Trajectory Clustering
 IEEE Transactions on Circuits and Systems for Video Technology, Vol.16, Issue
, 2006
"... Abstract—In this paper, we propose the use of lustering methods for automatic counting of pedestrians in video sequences. As input, we consider the output of those detection/tracking systems that overestimate the number of targets. Clustering techniques are applied to the resulting trajectories in o ..."
Abstract

Cited by 16 (0 self)
 Add to MetaCart
Abstract—In this paper, we propose the use of lustering methods for automatic counting of pedestrians in video sequences. As input, we consider the output of those detection/tracking systems that overestimate the number of targets. Clustering techniques are applied to the resulting trajectories in order to reduce the bias between the number of tracks and the real number of targets. The main hypothesis is that those trajectories belonging to the same human body are more similar than trajectories belonging to different individuals. Several data representations and different distance/similarity measures are proposed and compared, under a common hierarchical clustering framework, and both quantitative and qualitative results are presented. I.
Hierarchical Multiclassification with Predictive Clustering Trees in Functional Genomics
 the Workshop on Computational Methods in Bioinformatics at the 12th Portuguese Conference on Artificial Intelligence
, 2005
"... This paper investigates how predictive clustering trees can be used to predict gene function in the genome of the yeast Saccharomyces cerevisiae. We consider the MIPS FunCat classification scheme, in which each gene is annotated with one or more classes selected from a given functional class hi ..."
Abstract

Cited by 13 (3 self)
 Add to MetaCart
This paper investigates how predictive clustering trees can be used to predict gene function in the genome of the yeast Saccharomyces cerevisiae. We consider the MIPS FunCat classification scheme, in which each gene is annotated with one or more classes selected from a given functional class hierarchy. This setting presents two important challenges to machine learning: (1) each instance is labeled with a set of classes instead of just one class, and (2) the classes are structured in a hierarchy; ideally the learning algorithm should also take this hierarchical information into account. Predictive clustering trees generalize decision trees and can be applied to a wide range of prediction tasks by plugging in a suitable distance metric. We define an appropriate distance metric for hierarchical multiclassification and present experiments evaluating this approach on a number of data sets that are available for yeast.
Factors influencing the origins of colour categories
 Laboratory Vrije Universiteit Brussel
, 2002
"... van de academische graad van doctor in de wetenschappen, in het openbaar te verdedigen op vrijdag 8 maart 2002. Acknowledgements I started as a research assistant in the Artificial Intelligence Laboratory in autumn 1996. My first interests were into behavioural robotics and robot ecosystems. As a co ..."
Abstract

Cited by 9 (3 self)
 Add to MetaCart
van de academische graad van doctor in de wetenschappen, in het openbaar te verdedigen op vrijdag 8 maart 2002. Acknowledgements I started as a research assistant in the Artificial Intelligence Laboratory in autumn 1996. My first interests were into behavioural robotics and robot ecosystems. As a continuation to my “licentiaats ” thesis I started building a camera system to extend the sensory perception of the lab’s robots (Belpaeme and Birk, 1997a,b; Belpaeme, 1998; Birk and Belpaeme, 1998; Birk et al., 1998, 1999; Belpaeme and Birk, 2001). It was around that time when Luc Steels got interested in the origins of language. His early experiments formed the seed for what is now one of the most important paradigms for exploring linguistic interactions with computer simulations. Luc soon wanted more and had plans to implement a language experiment in the real world, for which I delivered the visual perception (Belpaeme et al., 1998; Belpaeme, 1999). This got me interested in visual features, and my research
Relational IBL in music with a new structural similarity measure
 In Proceedings of the International Conference on Inductive Logic Programming
, 2003
"... Abstract. It is well known that many hard tasks considered in machine learning and data mining can be solved in an rather simple and robust way with an instance and distancebased approach. In this paper we present another difficult task: learning, from large numbers of performances by concert pian ..."
Abstract

Cited by 8 (2 self)
 Add to MetaCart
Abstract. It is well known that many hard tasks considered in machine learning and data mining can be solved in an rather simple and robust way with an instance and distancebased approach. In this paper we present another difficult task: learning, from large numbers of performances by concert pianists, to play music expressively. We model the problem as a multilevel decomposition and prediction task. Motivated by structural characteristics of such a task, we propose a new relational distance measure that is a rather straightforward combination of two existing measures. Empirical evaluation shows that our approach is in general viable and our algorithm, named DISTALL, is indeed able to produce musically interesting results. The experiments also provide evidence of the success of ILP in a complex domain such as music performance: it is shown that our instancebased learner operating on structured, relational data outperforms a propositional kNN algorithm.
Distances and (indefinite) kernels for sets of objects
 In ICDM
, 2006
"... For various classification problems involving complex data, it is most natural to represent each training example as a set of vectors. While several distance measures for sets have been proposed, only a few kernels over these structures exist since it is difficult in general to design a positive sem ..."
Abstract

Cited by 7 (1 self)
 Add to MetaCart
For various classification problems involving complex data, it is most natural to represent each training example as a set of vectors. While several distance measures for sets have been proposed, only a few kernels over these structures exist since it is difficult in general to design a positive semidefinite (PSD) similarity function. The main disadvantage of most existing set kernels is that they are based on averaging, which might be inappropriate for problems where only specific elements of the two sets should determine the overall similarity. In this paper we propose a class of kernels for sets of vectors directly exploiting set distance measures and, hence, incorporating various semantics into set kernels and lending the power of regularization to learning in structural domains where natural distance functions exist. These kernels belong to two groups: (i) kernels in the proximity space induced by set distances and (ii) set distance substitution kernels (nonPSD in general). We report experimental results which show that our kernels compare favorably with kernels based on averaging and achieve results similar to other stateoftheart methods. At the same time our kernels bring systematically improvement over the naive way of exploiting distances. 1
Bridging the gap between distance and generalisation: Symbolic learning in metric spaces
, 2008
"... Distancebased and generalisationbased methods are two families of artificial intelligence techniques that have been successfully used over a wide range of realworld problems. In the first case, general algorithms can be applied to any data representation by just changing the distance. The metric ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
Distancebased and generalisationbased methods are two families of artificial intelligence techniques that have been successfully used over a wide range of realworld problems. In the first case, general algorithms can be applied to any data representation by just changing the distance. The metric space sets the search and learning space, which is generally instanceoriented. In the second case, models can be obtained for a given pattern language, which can be comprehensible. The generalityordered space sets the search and learning space, which is generally modeloriented. However, the concepts of distance and generalisation clash in many different ways, especially when knowledge representation is complex (e.g. structured data). This work establishes a framework where these two fields can be integrated in a consistent way. We introduce the concept of distancebased generalisation, which connects all the generalised examples in such a way that all of them are reachable inside the generalisation by using straight paths in the metric space. This makes the metric space and the generalityordered space coherent (or even dual). Additionally, we also introduce a definition of minimal distancebased generalisation that can be seen as the first formulation of the Minimum Description Length (MDL)/Minimum Message Length (MML) principle in terms of a distance function. We instantiate and develop the framework for the most common data representations and distances, where we show that consistent instances can be found for numerical data, nominal data, sets, lists, tuples, graphs, firstorder atoms and clauses. As a result, general learning methods that integrate the best from distancebased and generalisationbased methods can be defined and adapted to any specific problem by appropriately choosing the distance, the pattern language and the generalisation operator.
Computational methods for database repair by signed formulae
 Annals of Mathematics and Artificial Intelligence
, 2006
"... Abstract. We introduce a simple and practically efficient method for repairing inconsistent databases. Given a possibly inconsistent database, the idea is to properly represent the underlying problem, i.e., to describe the possible ways of restoring its consistency. We do so by what we call signed f ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
Abstract. We introduce a simple and practically efficient method for repairing inconsistent databases. Given a possibly inconsistent database, the idea is to properly represent the underlying problem, i.e., to describe the possible ways of restoring its consistency. We do so by what we call signed formulae, and show how the ‘signed theory ’ that is obtained can be used by a variety of offtheshelf computational models in order to rapidly and efficiently compute the corresponding solutions, i.e., consistent repairs of the database. 1.