Loi anglaise sur les marques de fabrique. Enregistrement. Formalités. Classification des produits. Taxes

[Loi]

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

AutoFACT: An Automatic Functional Annotation and Classification Tool

Affiliation: Centre Robert-Cedergren de l'Université de Montréal en bio-informatique et génomique & Département de biochimie, Université de Montréal

Schémas de classification et repérage des documents administratifs électroniques dans un contexte de gestion décentralisée des ressources informationnelles

Les employés d’un organisme utilisent souvent un schéma de classification personnel pour organiser les documents électroniques qui sont sous leur contrôle direct, ce qui suggère la difficulté pour d’autres employés de repérer ces documents et la perte possible de documentation pour l’organisme. Aucune étude empirique n’a été menée à ce jour afin de vérifier dans quelle mesure les schémas de classification personnels permettent, ou même facilitent, le repérage des documents électroniques par des tiers, dans le cadre d’un travail collaboratif par exemple, ou lorsqu’il s’agit de reconstituer un dossier. Le premier objectif de notre recherche était de décrire les caractéristiques de schémas de classification personnels utilisés pour organiser et classer des documents administratifs électroniques. Le deuxième objectif consistait à vérifier, dans un environnement contrôlé, les différences sur le plan de l’efficacité du repérage de documents électroniques qui sont fonction du schéma de classification utilisé. Nous voulions vérifier s’il était possible de repérer un document avec la même efficacité, quel que soit le schéma de classification utilisé pour ce faire. Une collecte de données en deux étapes fut réalisée pour atteindre ces objectifs. Nous avons d’abord identifié les caractéristiques structurelles, logiques et sémantiques de 21 schémas de classification utilisés par des employés de l’Université de Montréal pour organiser et classer les documents électroniques qui sont sous leur contrôle direct. Par la suite, nous avons comparé, à partir d'une expérimentation contrôlée, la capacité d’un groupe de 70 répondants à repérer des documents électroniques à l’aide de cinq schémas de classification ayant des caractéristiques structurelles, logiques et sémantiques variées. Trois variables ont été utilisées pour mesurer l’efficacité du repérage : la proportion de documents repérés, le temps moyen requis (en secondes) pour repérer les documents et la proportion de documents repérés dès le premier essai. Les résultats révèlent plusieurs caractéristiques structurelles, logiques et sémantiques communes à une majorité de schémas de classification personnels : macro-structure étendue, structure peu profonde, complexe et déséquilibrée, regroupement par thème, ordre alphabétique des classes, etc. Les résultats des tests d’analyse de la variance révèlent des différences significatives sur le plan de l’efficacité du repérage de documents électroniques qui sont fonction des caractéristiques structurelles, logiques et sémantiques du schéma de classification utilisé. Un schéma de classification caractérisé par une macro-structure peu étendue et une logique basée partiellement sur une division par classes d’activités augmente la probabilité de repérer plus rapidement les documents. Au plan sémantique, une dénomination explicite des classes (par exemple, par utilisation de définitions ou en évitant acronymes et abréviations) augmente la probabilité de succès au repérage. Enfin, un schéma de classification caractérisé par une macro-structure peu étendue, une logique basée partiellement sur une division par classes d’activités et une sémantique qui utilise peu d’abréviations augmente la probabilité de repérer les documents dès le premier essai.