Representações esparsas de sinais e algoritmos gananciosos

Mestrado em MatemáticaNesta dissertação apresentaremos novos resultados ligados ao uso de um algoritmo ganancioso, o dito Orthogonal Matching Pursuit (OMP), por forma a resolvermos problemas de aproximação esparsa em dicionários redundantes. Discutiremos, igualmente, uma modificação deste algoritmo, devida a Donoho, chamada Basis Matching Pursuit (BP). Apresentaremos uma condição (única) que assegura que ambos, OMP e BP, permitem recuperar sinais esparsos de forma exacta. Mais, mostraremos que ambos os algoritmos permitem efectuar a recuperação do sinal para uma vasta classe de dicionários. Com efeito, neste trabalho faremos um resumo de vários resultados recentes em BP, facilmente extensíveis ao caso de OMP. Adicionalmente, daremos também uma condição suficiente de garantia de que OMP pode recuperar átomos comuns a partir de todas as representações óptimas de um sinal não-esparso. Assim, OMP pode ser visto como um algoritmo de aproximação para o problema esparso num dicionário quasi-incoerente , isto é, para qualquer sinal dado, OMP permite calcular uma aproximação esparsa cujo erro não é muito pior do que o erro óptimo, e isto para o mesmo número de termos na aproximação.This dissertation presents new results regarding the usage of a greedy algorithm, the so-called Orthogonal Matching Pursuit (OMP), in order to solve sparse approximation problems over redundant dictionaries. We also discuss a modification of this algorithm, due to Donoho, denoted Basis Matching Pursuit (BP). We present a single sufficient condition under which both OMP and BP can recover a sparse signal in an exact way. Moreover, it is shown that both algorithms allow such a recovering for a wide class of dictionaries. Indeed, in this work we give several recent results on BP which can easily be extended to OMP. Furthermore, we also give a sufficient condition under which OMP can retrieve the common atoms from all optimal representations of a non-sparse signal. Thus, OMP can be viewed as an approximation algorithm for the sparse problem over a quasi-incoherent dictionary, that is, for every input signal, OMP can calculate a sparse approximation whose error is only a small factor worse than the optimal error, and this done with same the same number of terms in the approximation

Usefulness of Information for Goal Achievement

International audienceThis paper focuses on modelling information usefulness. More precisely, it aims at characterizing how useful a piece of information is for a cognitive agent which has some beliefs and goals. The paper presents three different approaches. We take Information Retrieval as a particular application domain and we compare some existing measures with the usefulness measure introduced in the paper

Testing Carlo Cipolla's Laws of Human Stupidity with Agent-Based Modeling

International audienceWe set up an agent-based simulation to test Carlo M. Cipolla's theory of human stupidity. In particular, we investigate under which hypotheses his theory is compatible with a well-corroborated theory like natural evolution, which we build into the model. We discover that there exist parameter settings which determine the emergence of stylized facts in line with Cipolla's theory. The assumptions corresponding to those parameter settings are intuitive and justified by common sense

Hybrid Possibilistic Conditioning for Revision under Weighted Inputs

International audienceWe propose and investigate new operators in the possi-bilistic belief revision setting, obtained as different combinations of the conditioning operators on models and countermodels, as well as of how weighted inputs are interpreted. We obtain a family of eight operators that essentially obey the basic postulates of revision, with a few slight differences. These operators show an interesting variety of behaviors, making them suitable to representing changes in the beliefs of an agent in different contexts

A Conceptual Representation of Documents and Queries for Information Retrieval Systems by Using Light Ontologies

International audienceThis article presents a vector space model approach to representing documents and queries, based on concepts instead of terms and using WordNet as a light ontology. Such representation reduces information overlap with respect to classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-hoc collections demonstrate the effectiveness of the proposed approach

Syntactic Computation of Hybrid Possibilistic Conditioning under Uncertain Inputs

International audienceWe extend hybrid possibilistic conditioning to deal with inputs consisting of a set of triplets composed of propositional formulas, the level at which the formulas should be accepted, and the way in which their models should be revised. We characterize such conditioning using elementary operations on possibility distributions. We then solve a difficult issue that concerns the syntactic computation of the revision of possibilistic knowledge bases, made of weighted formulas, using hybrid conditioning. An important result is that there is no extra computational cost in using hybrid possibilistic conditioning and in particular the size of the revised possibilistic base is polynomial with respect to the size of the initial base and the input

Vectorisation paramétrée des données textuelles

International audienceAutomatic processing of textual data enables users to analyze semi-automatically and on a large scale the data. This analysis is based on two successive processes: (i) representation of texts, (ii) gathering of textual data (clustering). The software described in this paper focuses on the first step of the process by offering expert a parameterized representation of textual data

Social Specialization of Space: Clustering Households on the French Riviera

International audienceThe aim of this paper is to estimate the extent of social specialization of residential space within the French Riviera metropolitan area. Unlike classical approaches, where social groups are pre-defined through given characteristics of households, our approach determines clusters of households inductively. Socio-demographic characteristics of households are thus measured through 16 different indicators. Clustering is then carried out through the optimization of two distinct criteria. Simulated annealing, simple and multi-objective Genetic Algorithm were adapted for this purpose and has produced pertinent results

A Neuro-Evolutionary Corpus-Based Method for Word Sense Disambiguation

International audienceWe propose a supervised approach to Word Sense Disambiguation based on Neural Networks combined with Evolutionary Algorithms. An established method to automatically design the structure and learn the connection weights of Neural Networks by means of an Evolutionary Algorithm is used to evolve a neural-network disambiguator for each polysemous word, against a dataset extracted from an annotated corpus. Two distributed encoding schemes, based on the orthography of words and characterized by different degrees of information compression, have been used to represent the context in which a word occurs. The performance of such encoding schemes has been compared. The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words. Comparison with the best entry of the Semeval-2007 competition has shown that the proposed approach is almost competitive with state-of-the-art WSD approaches

Syntactic Possibilistic Goal Generation

International audienceWe propose syntactic deliberation and goal election al-gorithms for possibilistic agents which are able to deal with incom-plete and imprecise information in a dynamic world. We show that the proposed algorithms are equivalent to their semantic counterparts already presented in the literature. We show that they lead to an ef-ficient implementation of a possibilistic BDI model of agency which integrates goal generation